US20130085079A1 - Cardiovascular Risk Event Prediction and Uses Thereof - Google Patents

Cardiovascular Risk Event Prediction and Uses Thereof Download PDF

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US20130085079A1
US20130085079A1 US13/631,567 US201213631567A US2013085079A1 US 20130085079 A1 US20130085079 A1 US 20130085079A1 US 201213631567 A US201213631567 A US 201213631567A US 2013085079 A1 US2013085079 A1 US 2013085079A1
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biomarkers
individual
biomarker
risk
event
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Rosalynn Dianne GILL
Stephen Alaric Williams
Alex A.E. Stewart
Robert MEHLER
Trudi FOREMAN
Britta SINGER
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Somalogic Inc
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Assigned to SOMALOGIC, INC. reassignment SOMALOGIC, INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: SINGER, Britta, WILLIAMS, STEPHEN ALARIC, GILL, Rosalynn Dianne, STEWART, ALEX A.E., FOREMAN, Trudi, MEHLER, Robert
Publication of US20130085079A1 publication Critical patent/US20130085079A1/en
Priority to US14/145,026 priority patent/US20150168423A1/en
Priority to US16/751,102 priority patent/US20200166523A1/en
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    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
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    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
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    • G01N2333/90—Enzymes; Proenzymes
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    • G01N2333/948—Hydrolases (3) acting on peptide bonds (3.4)
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    • G01N2333/964—Proteinases, i.e. endopeptidases (3.4.21-3.4.99) derived from animal tissue
    • G01N2333/96425—Proteinases, i.e. endopeptidases (3.4.21-3.4.99) derived from animal tissue from mammals
    • G01N2333/96427—Proteinases, i.e. endopeptidases (3.4.21-3.4.99) derived from animal tissue from mammals in general
    • G01N2333/9643—Proteinases, i.e. endopeptidases (3.4.21-3.4.99) derived from animal tissue from mammals in general with EC number
    • G01N2333/96486—Metalloendopeptidases (3.4.24)
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    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/60—Complex ways of combining multiple protein biomarkers for diagnosis

Definitions

  • the present application relates generally to the detection of biomarkers and a method of evaluating the risk of a future cardiovascular event in an individual and, more specifically, to one or more biomarkers, methods, devices, reagents, systems, and kits used to assess an individual for the prediction of risk of developing a Cardiovascular (CV) Event over a 5 year period.
  • Such Events include but are not limited to myocardial infarction, stroke, congestive heart failure or death.
  • Cardiovascular disease is the leading cause of death in the USA.
  • There are a number of existing and important predictors of risk of primary events (D'Agostino, R et al., “General Cardiovascular Risk Profile for Use in Primary Care: The Framingham Heart Study” Circulation 117:743-53 (2008); and Ridker, P. et al., “Development and Validation of Improved Algorithms for the Assessment of Global Cardiovascular Risk in Women” JAMA 297(6):611-619 (2007)) and secondary events (Shlipak, M. et al. “Biomarkers to Predict Recurrent Cardiovascular Disease: The Heart & Soul Study” Am. J. Med. 121:50-57 (2008)) which are widely used in clinical practice and therapeutic trials.
  • Risk factors for cardiovascular disease are widely used to drive the intensity and the nature of medical treatment, and their use has undoubtedly contributed to the reduction in cardiovascular morbidity and mortality that has been observed over the past two decades. These factors have routinely been combined into algorithms but unfortunately they do not capture all of the risk (the most common initial presentation for heart disease is still death). In fact they probably only capture half the risk. An area under the ROC curve of ⁇ 0.76 is typical for such risk factors, and again, is only about halfway between a coin-flip at 0.5 and perfection at 1.0.
  • Biomarker selection for the prediction of risk of having specific disease state or condition within a defined time period involves first the identification of markers that have a measurable and statistically significant difference in populations in which the event has or has not occurred during the time period for a specific medical application.
  • Biomarkers can include secreted or shed molecules that parallel disease or condition development or progression and readily diffuse into the blood stream from cardiovascular tissue or from surrounding tissues and circulating cells in response to a cardiovascular event.
  • the biomarker or set of biomarkers identified are generally clinically validated or shown to be a reliable indicator for the original intended use for which it was selected.
  • Biomarkers can include small molecules, peptides, proteins, and nucleic acids.
  • biomarkers A variety of methods have been utilized in an attempt to identify biomarkers and diagnose or predict the risk of having disease or a condition.
  • protein-based markers these include two-dimensional electrophoresis, mass spectrometry, and immunoassay methods.
  • nucleic acid markers these include mRNA expression profiles, microRNA profiles, FISH, serial analysis of gene expression (SAGE), large scale gene expression arrays, gene sequencing and genotyping (SNP or small variant analysis).
  • Sandwich immunoassays do not scale to high content, and thus biomarker discovery using stringent sandwich immunoassays is not possible using standard array formats. Lastly, antibody reagents are subject to substantial lot variability and reagent instability. The instant platform for protein biomarker discovery overcomes this problem.
  • sample preparation required to run a sufficiently powered study designed to identify and discover statistically relevant biomarkers in a series of well-defined sample populations is extremely difficult, costly, and time consuming.
  • fractionation a wide range of variability can be introduced into the various samples. For example, a potential marker could be unstable to the process, the concentration of the marker could be changed, inappropriate aggregation or disaggregation could occur, and inadvertent sample contamination could occur and thus obscure the subtle changes anticipated in early disease.
  • biomarker discovery and detection methods using these technologies have serious limitations for the identification of diagnostic or predictive biomarkers. These limitations include an inability to detect low-abundance biomarkers, an inability to consistently cover the entire dynamic range of the proteome, irreproducibility in sample processing and fractionation, and overall irreproducibility and lack of robustness of the method. Further, these studies have introduced biases into the data and not adequately addressed the complexity of the sample populations, including appropriate controls, in terms of the distribution and randomization required to identify and validate biomarkers within a target disease population.
  • Biomarker research based on 2D gels or mass spectrometry supports these notions. Very few useful biomarkers have been identified through these approaches. However, it is usually overlooked that 2D gel and mass spectrometry measure proteins that are present in blood at approximately 1 nM concentrations and higher, and that this ensemble of proteins may well be the least likely to change with disease or the development of a particular condition. Other than the instant biomarker discovery platform, proteomic biomarker discovery platforms that are able to accurately measure protein expression levels at much lower concentrations do not exist.
  • biochemical pathways for complex human biology. Many biochemical pathways culminate in or are started by secreted proteins that work locally within the pathology; for example, growth factors are secreted to stimulate the replication of other cells in the pathology, and other factors are secreted to ward off the immune system, and so on. While many of these secreted proteins work in a paracrine fashion, some operate distally in the body.
  • One skilled in the art with a basic understanding of biochemical pathways would understand that many pathology-specific proteins ought to exist in blood at concentrations below (even far below) the detection limits of 2D gels and mass spectrometry. What must precede the identification of this relatively abundant number of disease biomarkers is a proteomic platform that can analyze proteins at concentrations below those detectable by 2D gels or mass spectrometry.
  • cardiovascular events may be prevented by aggressive treatment if the propensity for such events can be accurately determined.
  • Existing multi-marker tests either require the collection of multiple samples from an individual or require that a sample be partitioned between multiple assays. Optimally, an improved test would require only a single blood, urine or other sample, and a single assay. Accordingly, a need exists for biomarkers, methods, devices, reagents, systems, and kits that enable the prediction of Cardiovascular Events within a 5 year period.
  • the present application includes biomarkers, methods, reagents, devices, systems, and kits for the prediction of risk of having a Cardiovascular (CV) Event within a 5 year period.
  • the biomarkers of the present application were identified using a multiplex SOMAmer-based assay which is described in detail in Examples 1 and 2.
  • this application describes a surprisingly large number of CV event biomarkers that are useful for the prediction of CV events.
  • the sample population used to discover biomarkers associated with the risk of a CV event was from the Heart & Soul Study, a prospective cohort study examining coronary artery disease progression in a population with pre-existing CV disease, including prior myocardial infarction, evidence of greater than 50% stenosis in 1 or more coronary vessels, exercise-induced ischemia by treadmill or nuclear testing or prior coronory revascularization.
  • the participants were recruited from the San Francisco Bay Area.
  • the CV event type and time for the study population are shown in Table 4.
  • Table 4 In identifying these CV event biomarkers, over 1000 proteins from over 900 individual samples were measured, some of which were at concentrations in the low femtomolar range. This is about four orders of magnitude lower than biomarker discovery experiments done with 2D gels and/or mass spectrometry.
  • CV event biomarkers While certain of the described CV event biomarkers are useful alone for prediction of risk of having a CV event, methods are described herein for the grouping of multiple subsets of the CV event biomarkers that are useful as a panel of biomarkers. Once an individual biomarker or subset of biomarkers has been identified, the prediction of risk of a CV event in an individual can be accomplished using any assay platform or format that is capable of measuring differences in the levels of the selected biomarker or biomarkers in a biological sample.
  • one or more biomarkers are provided for use either alone or in various combinations to predict the risk of the occurrence of a CV event within a 5 year time frame.
  • Exemplary embodiments include the biomarkers provided in Table 1, Col. 7, “PUBLIC_NAME”, which as noted above, were identified using a multiplex SOMAmer-based assay, as described generally in Example 1 and more specifically in Example 2.
  • the markers provided in Table 1 are useful in the prediction of risk of having a CV event within a 5 year time period.
  • CV event risk biomarkers While certain of the described CV event risk biomarkers are useful alone for the prediction of risk of a CV event within 5 years, methods are also described herein for the grouping of multiple subsets of the CV event risk biomarkers that are each useful as a panel of two or more biomarkers.
  • various embodiments of the instant application provide combinations comprising N biomarkers, wherein N is at least two biomarkers. In other embodiments, N is selected to be any number from 2-155 biomarkers.
  • N is selected to be any number from 2-7, 2-10, 2-15, 2-20, 2-25, 2-30, 2-35, 2-40, 2-45, 2-50, 2-55, or in successive increments of 5 for the upper limit of the range, up to and including 2-155.
  • N is selected to be any number from 3-7, 3-10, 3-15, 3-20, 3-25, 3-30, 3-35, 3-40, 3-45, 3-50, 3-55, or in successive increments of 5 for the upper limit of the range, up to and including 3-155.
  • N is selected to be any number from 4-7, 4-10, 4-15, 4-20, 4-25, 4-30, 4-35, 4-40, 4-45, 4-50, 4-55, or in successive increments of 5 for the upper limit of the range, up to and including 4-155.
  • N is selected to be any number from 5-7, 5-10, 5-15, 5-20, 5-25, 5-30, 5-35, 5-40, 5-45, 5-50, 5-55, or in successive increments of 5 for the upper limit of the range, up to and including 5-155.
  • N is selected to be any number from 6-10, 6-15, 6-20, 6-25, 6-30, 6-35, 6-40, 6-45, 6-50, 6-55, or in successive increments of 5 for the upper limit of the range, up to and including 6-155.
  • N is selected to be any number from 7-10, 7-15, 7-20, 7-25, 7-30, 7-35, 7-40, 7-45, 7-50, 7-55, or in successive increments of 5 for the upper limit of the range, up to and including 7-155.
  • N is selected to be any number from 8-10, 8-15, 8-20, 8-25, 8-30, 8-35, 8-40, 8-45, 8-50, 8-55, or in successive increments of 5 for the upper limit of the range, up to and including 8-155.
  • N is selected to be any number from 9-15, 9-20, 9-25, 9-30, 9-35, 9-40, 9-45, 9-50, 9-55, or in successive increments of 5 for the upper limit of the range, up to and including 9-155.
  • N is selected to be any number from 10-15, 10-20, 10-25, 10-30, 10-35, 10-40, 10-45, 10-50, 10-55, or in successive increments of 5 for the upper limit of the range, up to and including 10-155. It will be appreciated that N can be selected to encompass similar, but higher order, ranges.
  • cardiovascular events may be avoided by aggressive treatment if the propensity for such events can be accurately determined.
  • Prior art multi-marker tests either require the collection of multiple samples from an individual, or require that a sample be partitioned between multiple assays. It would be preferred to provide a prognostic assay that would require only a single biological sample, measured in a single assay, rather than multiple samples for different analyte types (lipids, proteins, metabolites) or panels of analytes.
  • the central benefit to a single sample test is simplicity at the point of use, since a test with multiple sample collections is more complex to administer and this forms a barrier to adoption.
  • An additional advantage derives from running that single sample in a single assay for multiple proteins.
  • a single assay should mitigate unwanted variation due to calibrating multiple assay results together.
  • the test which forms the basis of this application is such a “single sample, single assay” test.
  • This combination of single sample and single assay is a novel feature of this cardiovascular event risk test which addresses the logistic complexity of collecting multiple samples and the problems and biohazards involved in splitting samples into multiple aliquots for multiple independent analytical procedures.
  • Cardiovascular disease is known to involve multiple biological processes and tissues.
  • Well known examples of biological systems and processes associated with cardiovascular disease are inflammation, thrombosis, disease-associated angiogenesis, platelet activation, macrophage activation, liver acute response, extracellular matrix remodeling, and renal function. These processes can be observed as a function of gender, menopausal status, and age, and according to status of coagulation and vascular function. Since these systems communicate partially through protein based signaling systems, and multiple proteins may be measured in a single blood sample, the invention provides a single sample, single assay multiple protein based test focused on proteins from the specific biological systems and processes involved in cardiovascular disease.
  • one of the central functions of measuring risk for a cardiovascular event is to enable the assessment of progress in response to treatment and behavioral changes such as diet and exercise.
  • Current risk prediction methods such as the Framingham equation, include clearly correlated clinical covariate information, the largest such factor is the age of the subject. This makes the Framingham equation less useful for monitoring the change in an individual's risk, although it may be accurate for a population.
  • a novel feature of this CV event risk test is that it does not require age as a part of the prognostic model.
  • the subject invention is based on the premise that, within the biology of aging, there are causal factors which are variable and thus better used to assess risk.
  • the invention is premised on the belief that age itself is not a causal factor in the disease, and that age is acting as a surrogate or proxy for the underlying biology. While age is indeed prognostic of CV events, it cannot be used to assess individual improvement, and presumably the effect of age is mediated through biological function. This effect can be better determined through measurement of the relevant biology.
  • the proteins that are targeted are involved in the biology of the disease.
  • the invention captures the biological information that is reflected in the correlation between age and risk of a CV event. In fact, adding a factor for age to our model for risk based on proteins does not improve performance in predicting events.
  • the strategy to identify proteins from multiple processes involved in cardiovascular disease necessitated choosing parameters that provided a wide range/diversity of CV disease patients presenting with a variety of events or symptoms.
  • Events due to cardiovascular disease are heterogeneous, involving two main classes of event: thrombotic and CHF related events. Some presenting events may lack specific diagnostic information (e.g., death at home).
  • the inventive test was developed by measuring proteins involved from the biological processes associated with CV disease, on blood samples from a broad range of events. This strategy resulted in the inclusion of information from multiple processes involved in the disease (e.g., angiogenesis, platelet activation, macrophage activation, liver acute response, other lymphocyte inflammation, extracellular matrix remodeling, and renal function).
  • the chosen study population was a high risk group of subjects from the “Heart & Soul” study.
  • This set of subjects with a high rate of CV events it was possible to determine risk associated with protein measurements more accurately than would have been possible in the general population (within which events are rarer).
  • the development of the subject test on this high risk group permitted identification of protein biomarker combinations that could be generalized due to common biology.
  • the subject inventive test and biomarkers are likely to be effective beyond event prediction in a larger population than those individuals matching the entry criteria of the “Heart & Soul” study.
  • CV disease involves the blood coagulation system, inflammatory white blood cells and platelet activation.
  • the signals from the activation of these systems in the body can be obscured due to common errors in sample preparation which lead to platelets and white blood cells being only partially spun down from plasma samples. If these cells are not completely spun down, they may be lysed by freeze-thaw when the samples are shipped and assayed.
  • conventionally prepared samples contain whole cells and platelets after freeze-thaw.
  • any whole cells would lyse and interfere with the detection of proteins characteristic of the disease processes of in vivo activation of platelets and monocytes.
  • an additional step of re-spinning the samples after thawing is conducted prior to the assay.
  • This additional spin step can remove platelets and monocytes which would otherwise prevent the identification of biomarkers related to platelet and monocyte activation.
  • the additional step to remove the insoluble and cellular components of the samples represents an advance for a cardiovascular event risk test which is believed to not be described in the prior art.
  • GFR The measurement of GFR is clearly useful in predicting the risk of a CV event.
  • the clinical measurement of GFR involves urine collection over 24 hours, which does not meet the subject standard of a “single sample, single assay” test.
  • Other estimates of GFR are less onerous; however, to meet the goal of a “single sample” prognostic test, the strategy underlying the subject invention sought the use of the protein measurements themselves to provide GFR information for the risk analysis.
  • the protein ESAM strongly predicts CV event risk due to its correlation with GFR. After correcting the measurements of the protein ESAM to remove the correlation with estimated GFR, ESAM is no longer predictive of risk.
  • This use of a protein such as ESAM to convey the biological signal related to GFR in a “single sample, single assay” represents a novel advance for the prognosis of a CV event.
  • the identification of the Table 3 biomarkers involved selection for proteins that could work together in the prognosis of a CV event.
  • the subject invention provides a statistical analysis procedure in which proteins were screened for both for their individual prognostic power, and also, crucially, the capability of the proteins to work together synergistically to improve the prognostic value of the combination. Multiple independent biological processes are represented in the Table 3 ten marker protein model provided herein.
  • the invention comprises a method for evaluating the risk of a future cardiovascular (CV) Event within a 5 year time period in a population.
  • the selection of a population is such that the population is characterized as having no prior history of cardiovascular disease.
  • the population may be selected such that it is characterized as having a prior history of cardiovascular disease.
  • the prior history can comprise prior myocardial infarction, angiographic evidence of greater than 50% stenosis in 1 or more coronary vessles, exercise-induced ischemia by treadmill or nuclear testing or prior coronary revascularization.
  • the population may be selected such that it is characterized by genetic risk factors comprising mutations, single nucleotide polymorphisms and insertion/deletions. Such genetic risk factors can be used to complement the evaluation of risk.
  • the evaluation of risk of a CV event can be measured on a dynamic scale that is responsive to change over time in response to interventions comprising therapeutics, nutritional programs, supplementation, lifestyle modification, smoking cessation programs and disease management protocols.
  • the foregoing methods relating to evaluation of CV event risk over a 5 year period can be used to allocate the individuals into increased or decreased disease management programs, based on their biomarker values.
  • the method can also be used to stratify the individuals into different risk bands relating to life insurance coverage depending on said biomarker value. Also, it can be used for evaluation of CV event risk in order to stratify the individuals into different risk bands relating to health insurance coverage depending on said biomarker value. Additionally, it can be used to assess potential candidates for partnership depending on biomarker values.
  • CV event risk prediction can be used to: predict medical resource consumption of the population based on the biomarker values; use the biomarker value of the individual as an entry criterion for clinical trials of CV therapeutics; prediction of efficacy of clinical trial results based on said biomarker value; use the biomarker value for cardiovascular safety surveillance of a CV therapeutic or any therapeutic agent; use the biomarker value as a surrogate endpoint of efficacy of CV therapeutics; and/or monitor compliance with any intervention, dietary or therapeutic protocol based on said biomarker value.
  • CV safety surveillance of a CV therapeutic or any therapeutic agent such surveillance is important in large, costly, required phase 3 CV safety studies for CV therapeutic and non-cardiovascular drugs for nearly every chronic use.
  • the subject method for evaluating a CV event risk can also be used to select or refer the individual for other diagnostic procedures based on said biomarker value. Additionally, the subject method can be used to select a CV therapeutic based on biomarker value.
  • the biomarker values can be detected by performing an in vitro assay.
  • the in vitro assay can involve at least one capture reagent corresponding to each of the biomarkers, and further can include at least one capture reagent from the group consisting of SOMAmers, antibodies, and a nucleic acid probe.
  • the capture reagent is a SOMAmer.
  • the in vitro assay can be selected from the group consisting of an immunoassay, a SOMAmer-based assay, a histological or cytological assay, and an mRNA expression level assay.
  • the biological sample can be whole blood, plasma, serum, urine or the like.
  • the biological sample is serum, plasma or urine.
  • the individual can be a mammal, and in particular, a human.
  • the invention provides for a computer-implemented method for evaluating the risk of a cardiovascular (CV) Event.
  • the result of the evaluation of risk of a CV event for the individual can be displayed a computer display.
  • the invention comprises a computer program product for evaluating the risk of a CV event.
  • the computer program product can include a computer readable medium embodying program code executable by a processor of a computing device or system, where the program code comprises: code that retrieves data attributed to a biological sample from an individual, wherein the data comprises biomarker values that each correspond to one of at least N biomarkers selected from Table 1, where the biomarkers were detected in the biological sample; and code that executes a classification method that indicates a result of the evaluation of risk for a CV event of the individual as a function of said biomarker values.
  • the classification method can use a continuous score or measure or risk metric.
  • the classification method can also use two or more classes.
  • the subject invention further comprises a method for screening an individual for evaluation of risk of a CV event.
  • the detection of the biomarker values can be done in an in vitro assay.
  • Such in vitro assay can include at least one capture reagent corresponding to each of the biomarkers, and can further comprise the selection of the at least one capture reagent from the group consisting of SOMAmers, antibodies, and a nucleic acid probe.
  • the at least one capture reagent is a SOMAmer.
  • the in vitro assay can be selected from an immunoassay, a SOMAmer-based assay, a histological or cytological assay, and an mRNA expression level assay.
  • the biological sample can be selected from whole blood, plasma, serum, urine and the like.
  • the biological sample is serum, plasma or urine.
  • the individual can be a mammal, and is preferably a human.
  • At least one item of additional biomedical information can include, but is not limited to, any of the following: (a) information corresponding to the presence of cardiovascular risk factors including one or more of a prior myocardial infarction, angiographic evidence of greater than 50% stenosis in one or more coronary vessels, exercise-induced ischemia by treadmill or nuclear testing or prior coronary revascularization; (b) information corresponding to physical descriptors of said individual; (c) information corresponding to a change in weight of said individual; (d) information corresponding to the ethnicity of said individual; (e) information corresponding to the gender of said individual; (f) information corresponding to said individual's smoking history; (g) information corresponding to said individual's alcohol use history; (h) information corresponding to said individual's occupational history; (i) information corresponding to the following: (a) information corresponding to the presence of cardiovascular risk factors including one or more of a prior myocardial infarction, angiographic evidence of greater than 50% stenosis in one or more
  • the invention comprises a method for screening an individual in a population by evaluating or prognosing the risk of a future CV event within a 5 year period, by detecting, in a biological sample from the individual, a biomarker value for angiopoietin 2, and determining the risk of a future CV event on the basis of the angiopoietin 2 biomarker value.
  • the biomarker value can be expressed as a measurement score or a classification into one of a plurality of classifications.
  • the subject invention also comprises adding to the method, before the determining step, the step comprising providing information regarding the individual's use of a statin.
  • the determining of the risk of a future CV event is on the basis of the angiopoietin 2 biomarker value and the statin information.
  • the angiopoietin 2 is surprisingly useful in prognosing a secondary cardiovascular event for individuals on statins.
  • Statins have been reported in the prior art to not only reduce the risk of a secondary cardiovascular event, but also cause an increase in angiopoietin 2. This rise in angiopoietin 2 would have been expected to negate it's use as a biomarker.
  • angiopoietin 2 has demonstrated that it is a good marker for prediction of secondary cardiovascular events in high risk individuals.
  • the method of detecting angiopoietin 2 biomarker value can comprise the further step of: detecting in the biological sample a biomarker value for one or more of MMP7, CHRDL1, MATN2, PSA-ACT biomarkers or a combination thereof.
  • the invention provides a panel of biomarkers for screening an individual in a population by evaluating or prognosing the risk of a future CV event within a 5 year period.
  • the panel includes at least the angiopoietin 2 biomarker.
  • This panel can further include one or more of the MMP7, CHRDL1, MATN2, PSA-ACT biomarkers or any combination thereof.
  • the invention provides a method for screening an individual by evaluating the risk of a future CV event within a 5 year period, wherein the evaluating comprises a differential prognosis of a thrombotic event or congestive heart failure (CHF) event.
  • This method comprises: detecting in a biological sample from the individual of the population, biomarker values that each correspond to GPVI biomarker for prognosis of the thrombotic event, and MATN2 biomarker for the prognosis of the CHF event.
  • the thrombotic event can include any of a myocardial infarction (MI), transient ischemic attack (TIA), stroke, acute coronary syndrome and a need for coronary re-vascularization.
  • MI myocardial infarction
  • TIA transient ischemic attack
  • stroke acute coronary syndrome
  • a panel of biomarkers for screening an individual in a population by evaluating or prognosing the risk of a future CV event within a 5 year period, wherein the panel comprises a GPVI biomarker and a MATN2 biomarker.
  • the panel can additionally include at least one of N biomarkers selected from the group consisting of the biomarkers set forth in Table 3.
  • CV disease Multiple classes of treatment for CV disease are available, reflecting the variety of biological systems involved. For example, anti-thrombotic, platelet inhibitor, lipid metabolism, fluid and electrolyte balance medications, and beta blockers, have been used in the treatment of CV disease. In order to guide treatment, it is useful to identify not only the overall risk, but also distinguish the class of event indicated by the biology.
  • the foregoing method using MATN2 and GPVI allows for distinguishing the probable event classes of thrombotic events and CHF events.
  • the GPVI is more specific to the development of the thrombotic event, and the MATN2 is more specific to CHF events.
  • the specifity of GPVI for thrombotic events is demonstrated in FIGS. 8A and 8B .
  • FIGS. 9A and 9B The specificity of MATN2 for prognosis of CHF is illustrated in FIGS. 9A and 9B . These differences can be interpreted in terms of the related biological processes. This multiple protein based test can therefore provide the patient with information to distinguish the risk of developing CHF versus the risk of thrombotic events. This is a significant and important feature of the invention that is believed not to be described in the prior art.
  • the subject method In addition to providing prognosis of CV event risk based on protein measurements alone, the subject method also provides the advantage of a more complete picture derived from taking into account simple information such as gender, medication, other markers such as LDL cholesterol, HDL cholesterol, total cholesterol, and other conditions such as diabetes. Such models can be built upon the existing Table 3 ten protein model introduced here.
  • kits for screening an individual in a population by evaluating or prognosing the risk of a future CV event within a 5 year period includes the following components: at least one of the biomarkers set forth in Table 1; at least one corresponding capture reagent, wherein each of the corresponding capture reagents is specific to the selected biomarkers; and a signal generating material, said material being specific to the selected corresponding biomarkers and/or corresponding capture reagents, wherein each signal is activated upon binding of each capture reagent to the corresponding biomarker.
  • the capture reagents of the kits can be any one or more of SOMAmers, antibodies, and nucleic acid probes or a combination thereof.
  • the kit can also include instructions or one or more software or computer program products for classifying the individual from whom the biological sample was obtained, as either having or not having increased risk of a CV event.
  • the subject invention comprises a classifier comprising the biomarkers of Table 1, col. 7, Table 2 or Table 3.
  • FIG. 1A is a flowchart for an exemplary method for the prediction of a CV event in a biological sample.
  • FIG. 1B is a flowchart for an exemplary method for the prediction of a CV event in a biological sample using a na ⁇ ve Bayes classification method.
  • FIG. 2A shows a Principal component analysis for a subgroup of cases with Events and controls with no CV events within 6 months. The cases with events are partially separated from the controls along the vertical axis.
  • FIG. 2B shows a DSGA analysis for a subgroup of cases with Events and controls with no CV events within 6 months. The cases with events are partially separated from the controls along the horizontal axis.
  • FIG. 3 provides a risk score analysis for the study population. This score was calculated by building a simple Cox proportional hazard model using the logarithm of the measurements of the ten proteins in Table 3. The population was split into quintiles based on this score. The Kaplan Meier plots in FIG. 3 demonstrate how these quintiles differ in the proportion of the individual experiencing a cardiovascular event or death for various event types.
  • FIG. 3A shows the Kaplan Meier plots of all deaths and cardiovascular events of the study population, with the population split into quintiles based on the Table 3 protein scores.
  • FIG. 3B shows the Kaplan Meier plots for the cases of CV events: unclassified deaths, those deaths without a known proximal cause such as MI or CHF (congestive heart failure), with the population split into quintiles based on the Table 3 protein scores.
  • FIG. 3C shows the Kaplan Meier plots for the cases of CV events: incident CHF with the population split into quintiles based on the Table 3 protein scores.
  • FIG. 3D shows the Kaplan Meier plots for the cases of CV events: CHF recurrence for chronic CHF patients (those with a previous diagnosis of CHF) with the population split into quintiles based on the Table 3 protein scores.
  • FIG. 3E shows the Kaplan Meier plots for the cases of CV events: thrombotic event (MI+stroke) with the population split into quintiles based on the Table 3 protein scores.
  • FIG. 3F shows the Kaplan Meier plots for the cases of CV events: all CHF, with the population split into quintiles based on the Table 3 protein scores.
  • FIG. 4 illustrates an exemplary computer system for use with various computer-implemented methods described herein.
  • FIG. 5 is a flowchart for a method of indicating evaluating risk of a CV event in accordance with one embodiment.
  • FIG. 6 is a flowchart for a method of evaluating risk of a CV event in accordance with one embodiment.
  • FIG. 7 illustrates an exemplary aptamer assay that can be used to detect one or more CV event biomarkers in a biological sample.
  • FIG. 8 shows the Kaplan Meier plots based on GPVI, one of the ten proteins in Table 3, demonstrating that this protein distinguishes between thrombotic and CHF events.
  • the population is split into quartiles of GPVI.
  • FIG. 8A shows the highest quartile of GPVI is prognostic for thrombotic cardiovascular events.
  • FIG. 8B shows that the quartiles of GPVI have little or no discriminative ability to forecast CHF events.
  • FIG. 9 shows the Kaplan Meier plots based on MATN2, one of the ten proteins in Table 3, demonstrating that this protein distinguishes between thrombotic and CHF events.
  • the population is split into quartiles of MATN2.
  • FIG. 9A shows that the quartiles of MATN2 are not prognostic for thrombotic cardiovascular events.
  • FIG. 9B shows that individuals from the highest quartile of MATN2 have a higher rate of CHF events.
  • FIG. 10 shows the Kaplan Meier plots of all 538 subjects taking statin medication showing that those individuals in the 4th quartile of the population distribution for angiopoietin-2 suffer cardiovascular events at an increased rate compared to those not in the 4th Quartile for angiopoietin-2.
  • angiopoietin-2 is a useful biomarker of the risk of CV events.
  • FIG. 11 shows the Kaplan Meier plots of all 538 subjects taking statin medication showing that CHRDL1 is associated with the event free survival of cardiovascular events in individuals treated with statin.
  • CHRDL1 is a useful biomarker of the risk of CV events.
  • the term “about” represents an insignificant modification or variation of the numerical value such that the basic function of the item to which the numerical value relates is unchanged.
  • the terms “comprises,” “comprising,” “includes,” “including,” “contains,” “containing,” and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, product-by-process, or composition of matter that comprises, includes, or contains an element or list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, product-by-process, or composition of matter.
  • the present application includes biomarkers, methods, devices, reagents, systems, and kits for the prediction of risk of CV events within a defined period of time, such as 5 years.
  • Cardiovascular Event means a failure or malfunction of any part of the circulatory system.
  • “Cardiovascular Event” means stroke, transient ischemic attack (TIA), myocardial infarction (MI), sudden death attributable to malfunction of the circulatory system, and/or heart failure.
  • TIA transient ischemic attack
  • MI myocardial infarction
  • Cardiovascular Event means any of the foregoing malfunctions and/or unstable angina, need for stent or angioplasty, or the like.
  • Cardiovascular Events include “Congestive Heart Failure” or “CHF” and “thrombotic events.”
  • Thrombotic Events include MIs, transient ischemic attacks (TIA), stroke, acute coronary syndrome and need for coronary re-vascularization.
  • one or more biomarkers are provided for use either alone or in various combinations to evaluate the risk of a future CV event within a 5 year time period with CV events defined as myocardial infarction, stroke, death and congestive heart failure.
  • Thrombotic events FIG. 3 e
  • exemplary embodiments include the biomarkers provided in Table 1, Col. 7, which were identified using a multiplex SOMAmer-based assay that is described generally in Example 1 and more specifically in Example 2.
  • Table 1, Col. 7 sets forth the findings obtained from analyzing hundreds of individual blood samples from patients who have had a CV event within a 6 month-10 year time frame (Event Positive) after initial blood draw (time point 1), and hundreds of equivalent individual blood samples from individuals who did not have a CV event within that time frame (Event Negative).
  • the potential biomarkers were measured in individual samples rather than pooling the Event Positive and Event Negative blood samples; this allowed a better understanding of the individual and group variations in the phenotypes associated with the presence and absence of a CV event. Since over 1000 protein measurements were made on each sample, and several hundred samples from each of the Event Positive and the Event Negative populations were individually measured, the biomarkers reported in Table 1, Col. 7 resulted from an analysis of an uncommonly large set of data.
  • Table 1 Col. 7 lists the 155 biomarkers found to be useful in stratifying the population of individuals according to their propensity to exhibit a future CV event in the period of 0-5 years after blood sample was drawn.
  • the Kaplan-Meier curves in FIGS. 3A-3F show a strong dependence of event risk upon quintile of a score determined by a small subset of such biomarkers, as listed in Table 3.
  • CV event biomarkers While certain of the described CV event biomarkers are useful alone for evaluating the risk of a CV event, methods are also described herein for the grouping of multiple subsets of the CV event biomarkers, where each grouping or subset selection is useful as a panel of three or more biomarkers, interchangeably referred to herein as a “biomarker panel” and a panel.
  • biomarker panel and a panel.
  • various embodiments of the instant application provide combinations comprising N biomarkers, wherein N is at least two biomarkers. In other embodiments, N is selected from 2-155 biomarkers.
  • N is selected to be any number from 2-7, 2-10, 2-15, 2-20, 2-25, 2-30, 2-35, 2-40, 2-45, 2-50, 2-55, or in successive increments of 5 for the upper limit of the range, up to and including 2-155.
  • N is selected to be any number from 3-7, 3-10, 3-15, 3-20, 3-25, 3-30, 3-35, 3-40, 3-45, 3-50, 3-55, or in successive increments of 5 for the upper limit of the range, up to and including 3-155.
  • N is selected to be any number from 4-7, 4-10, 4-15, 4-20, 4-25, 4-30, 4-35, 4-40, 4-45, 4-50, 4-55, or in successive increments of 5 for the upper limit of the range, up to and including 4-155.
  • N is selected to be any number from 5-7, 5-10, 5-15, 5-20, 5-25, 5-30, 5-35, 5-40, 5-45, 5-50, 5-55, or in successive increments of 5 for the upper limit of the range, up to and including 5-155.
  • N is selected to be any number from 6-10, 6-15, 6-20, 6-25, 6-30, 6-35, 6-40, 6-45, 6-50, 6-55, or in successive increments of 5 for the upper limit of the range, up to and including 6-155.
  • N is selected to be any number from 7-10, 7-15, 7-20, 7-25, 7-30, 7-35, 7-40, 7-45, 7-50, 7-55, or in successive increments of 5 for the upper limit of the range, up to and including 7-155.
  • N is selected to be any number from 8-10, 8-15, 8-20, 8-25, 8-30, 8-35, 8-40, 8-45, 8-50, 8-55, or in successive increments of 5 for the upper limit of the range, up to and including 8-155.
  • N is selected to be any number from 9-15, 9-20, 9-25, 9-30, 9-35, 9-40, 9-45, 9-50, 9-55, or in successive increments of 5 for the upper limit of the range, up to and including 9-155.
  • N is selected to be any number from 10-15, 10-20, 10-25, 10-30, 10-35, 10-40, 10-45, 10-50, 10-55, or in successive increments of 5 for the upper limit of the range, up to and including 10-155. It will be appreciated that N can be selected to encompass similar, but higher order, ranges.
  • the number of biomarkers useful for a biomarker subset or panel is based on the sensitivity and specificity value for the particular combination of biomarker values.
  • sensitivity and “specificity” are used herein with respect to the ability to correctly classify an individual, based on one or more biomarker values detected in their biological sample, as having an increased risk of having a CV Event within 5 years or not having increased risk of having a CV event within the same time period.
  • Stress indicates the performance of the biomarker(s) with respect to correctly classifying individuals that have increased risk of a CV event.
  • Specificity indicates the performance of the biomarker(s) with respect to correctly classifying individuals who do not have increased risk of a CV event.
  • 85% specificity and 90% sensitivity for a panel of markers used to test a set of Event Negative samples and Event Positive samples indicates that 85% of the control samples were correctly classified as Event Negative samples by the panel, and 90% of the Event Positive samples were correctly classified as Event Positive samples by the panel.
  • scores may be reported on a continuous range, with a threshold of high, intermediate or low risk of a CV event, with thresholds determined based on clinical findings.
  • the CV event risk biomarkers identified herein represent a exceedingly large number of choices for subsets or panels of biomarkers that can be used to predict the risk of a CV event. Selection of the desired number of such biomarkers depends on the specific combination of biomarkers chosen. It is important to remember that panels of biomarkers for predicting CV event risk may also include biomarkers not found in Table 1, Col. 7, and that the inclusion of additional biomarkers not found in Table 1, Col. 7 may reduce the number of biomarkers in the particular subset or panel that is selected from Table 1, Col. 7. The number of biomarkers from Table 1, Col. 7 used in a subset or panel may also be reduced if additional biomedical information is used in conjunction with the biomarker values to establish acceptable threshold values for a given assay.
  • biomarkers to be used in a subset or panel of biomarkers Another factor that can affect the number of biomarkers to be used in a subset or panel of biomarkers is the procedures used to obtain biological samples from individuals who are being assessed for risk of a CV event. In a carefully controlled sample procurement environment, the number of biomarkers necessary to meet desired sensitivity and specificity and/or threshold values will be lower than in a situation where there can be more variation in sample collection, handling and storage.
  • a biological sample is obtained from an individual or individuals of interest.
  • the biological sample is then assayed to detect the presence of one or more (N) biomarkers of interest and to determine a biomarker value for each of said N biomarkers (referred to in FIG. 1B as marker RFU).
  • N one or more biomarkers of interest
  • RFU a biomarker value for each of said N biomarkers
  • the marker scores are then combined to provide a total diagnostic score, which indicates the likelihood that the individual from whom the sample was obtained has high, medium or low risk of a CV event, particularly when reported on a continuous range.
  • Biological sample “sample”, and “test sample” are used interchangeably herein to refer to any material, biological fluid, tissue, or cell obtained or otherwise derived from an individual. This includes blood (including whole blood, leukocytes, peripheral blood mononuclear cells, buffy coat, plasma, and serum), dried blood spots (e.g., obtained from infants), sputum, tears, mucus, nasal washes, nasal aspirate, breath, urine, semen, saliva, peritoneal washings, ascites, cystic fluid, meningeal fluid, amniotic fluid, glandular fluid, pancreatic fluid, lymph fluid, pleural fluid, nipple aspirate, bronchial aspirate, bronchial brushing, synovial fluid, joint aspirate, organ secretions, cells, a cellular extract, and cerebrospinal fluid.
  • blood including whole blood, leukocytes, peripheral blood mononuclear cells, buffy coat, plasma, and serum
  • dried blood spots e.g.,
  • a blood sample can be fractionated into serum, plasma or into fractions containing particular types of blood cells, such as red blood cells or white blood cells (leukocytes).
  • a sample can be a combination of samples from an individual, such as a combination of a tissue and fluid sample.
  • biological sample also includes materials containing homogenized solid material, such as from a stool sample, a tissue sample, or a tissue biopsy, for example.
  • biological sample also includes materials derived from a tissue culture or a cell culture.
  • any suitable methods for obtaining a biological sample can be employed; exemplary methods include, e.g., phlebotomy, swab (e.g., buccal swab), and a fine needle aspirate biopsy procedure.
  • tissue susceptible to fine needle aspiration include lymph node, lung, lung washes, BAL (bronchoalveolar lavage), thyroid, breast, pancreas and liver.
  • Samples can also be collected, e.g., by micro dissection (e.g., laser capture micro dissection (LCM) or laser micro dissection (LMD)), bladder wash, smear (e.g., a PAP smear), or ductal lavage.
  • a “biological sample” obtained or derived from an individual includes any such sample that has been processed in any suitable manner after being obtained from the individual.
  • a biological sample can be derived by taking biological samples from a number of individuals and pooling them or pooling an aliquot of each individual's biological sample.
  • the pooled sample can be treated as a sample from a single individual and if an increased or decreased risk of a CV event is established in the pooled sample, then each individual biological sample can be re-tested to determine which individual/s have an increased or decreased risk of a CV event.
  • the biological sample can be urine.
  • Urine samples provide certain advantages over blood or serum samples. Collecting blood or plasma samples through venipuncture is more complex than is desirable, can deliver variable volumes, can be worrisome for the patient, and involves some (small) risk of infection. Also, phlebotomy requires skilled personnel. The simplicity of collecting urine samples can lead to more widespread application of the subject methods.
  • PSA-ACT and Plasminogen have been found to demonstrate variability between individuals in urine. This indicates that the quantification of these biomarkers in urine can also be useful in the method of screening an individual for risk of a CV event.
  • these five proteins provide for a simple urine-based test to be used in the subject methods of screening individuals for risk of a CV event.
  • the phrase “data attributed to a biological sample from an individual” is intended to mean that the data in some form derived from, or were generated using, the biological sample of the individual.
  • the data may have been reformatted, revised, or mathematically altered to some degree after having been generated, such as by conversion from units in one measurement system to units in another measurement system; but, the data are understood to have been derived from, or were generated using, the biological sample.
  • Target “target molecule”, and “analyte” are used interchangeably herein to refer to any molecule of interest that may be present in a biological sample.
  • a “molecule of interest” includes any minor variation of a particular molecule, such as, in the case of a protein, for example, minor variations in amino acid sequence, disulfide bond formation, glycosylation, lipidation, acetylation, phosphorylation, or any other manipulation or modification, such as conjugation with a labeling component, which does not substantially alter the identity of the molecule.
  • a “target molecule”, “target”, or “analyte” is a set of copies of one type or species of molecule or multi-molecular structure.
  • Target molecules refer to more than one such set of molecules.
  • exemplary target molecules include proteins, polypeptides, nucleic acids, carbohydrates, lipids, polysaccharides, glycoproteins, hormones, receptors, antigens, antibodies, affybodies, antibody mimics, viruses, pathogens, toxic substances, substrates, metabolites, transition state analogs, cofactors, inhibitors, drugs, dyes, nutrients, growth factors, cells, tissues, and any fragment or portion of any of the foregoing.
  • polypeptide As used herein, “polypeptide,” “peptide,” and “protein” are used interchangeably herein to refer to polymers of amino acids of any length.
  • the polymer may be linear or branched, it may comprise modified amino acids, and it may be interrupted by non-amino acids.
  • the terms also encompass an amino acid polymer that has been modified naturally or by intervention; for example, disulfide bond formation, glycosylation, lipidation, acetylation, phosphorylation, or any other manipulation or modification, such as conjugation with a labeling component.
  • polypeptides containing one or more analogs of an amino acid including, for example, unnatural amino acids, etc.
  • Polypeptides can be single chains or associated chains. Also included within the definition are preproteins and intact mature proteins; peptides or polypeptides derived from a mature protein; fragments of a protein; splice variants; recombinant forms of a protein; protein variants with amino acid modifications, deletions, or substitutions; digests; and post-translational modifications, such as glycosylation, acetylation, phosphorylation, and the like.
  • marker and “biomarker” are used interchangeably to refer to a target molecule that indicates or is a sign of a normal or abnormal process in an individual or of a disease or other condition in an individual. More specifically, a “marker” or “biomarker” is an anatomic, physiologic, biochemical, or molecular parameter associated with the presence of a specific physiological state or process, whether normal or abnormal, and, if abnormal, whether chronic or acute. Biomarkers are detectable and measurable by a variety of methods including laboratory assays and medical imaging.
  • a biomarker is a protein
  • biomarker value As used herein, “biomarker value”, “value”, “biomarker level”, and “level” are used interchangeably to refer to a measurement that is made using any analytical method for detecting the biomarker in a biological sample and that indicates the presence, absence, absolute amount or concentration, relative amount or concentration, titer, a level, an expression level, a ratio of measured levels, or the like, of, for, or corresponding to the biomarker in the biological sample.
  • the exact nature of the “value” or “level” depends on the specific design and components of the particular analytical method employed to detect the biomarker.
  • biomarker When a biomarker indicates or is a sign of an abnormal process or a disease or other condition in an individual, that biomarker is generally described as being either over-expressed or under-expressed as compared to an expression level or value of the biomarker that indicates or is a sign of a normal process or an absence of a disease or other condition in an individual.
  • Up-regulation”, “up-regulated”, “over-expression”, “over-expressed”, and any variations thereof are used interchangeably to refer to a value or level of a biomarker in a biological sample that is greater than a value or level (or range of values or levels) of the biomarker that is typically detected in similar biological samples from healthy or normal individuals.
  • the terms may also refer to a value or level of a biomarker in a biological sample that is greater than a value or level (or range of values or levels) of the biomarker that may be detected at a different stage of a particular disease.
  • Down-regulation “down-regulated”, “under-expression”, “under-expressed”, and any variations thereof are used interchangeably to refer to a value or level of a biomarker in a biological sample that is less than a value or level (or range of values or levels) of the biomarker that is typically detected in similar biological samples from healthy or normal individuals.
  • the terms may also refer to a value or level of a biomarker in a biological sample that is less than a value or level (or range of values or levels) of the biomarker that may be detected at a different stage of a particular disease.
  • a biomarker that is either over-expressed or under-expressed can also be referred to as being “differentially expressed” or as having a “differential level” or “differential value” as compared to a “normal” expression level or value of the biomarker that indicates or is a sign of a normal process or an absence of a disease or other condition in an individual.
  • “differential expression” of a biomarker can also be referred to as a variation from a “normal” expression level of the biomarker.
  • differential gene expression and “differential expression” are used interchangeably to refer to a gene (or its corresponding protein expression product) whose expression is activated to a higher or lower level in a subject suffering from a specific disease or condition, relative to its expression in a normal or control subject.
  • the terms also include genes (or the corresponding protein expression products) whose expression is activated to a higher or lower level at different stages of the same disease or condition. It is also understood that a differentially expressed gene may be either activated or inhibited at the nucleic acid level or protein level, or may be subject to alternative splicing to result in a different polypeptide product.
  • Differential gene expression may include a comparison of expression between two or more genes or their gene products; or a comparison of the ratios of the expression between two or more genes or their gene products; or even a comparison of two differently processed products of the same gene, which differ between normal subjects and subjects suffering from a disease; or between various stages of the same disease.
  • Differential expression includes both quantitative, as well as qualitative, differences in the temporal or cellular expression pattern in a gene or its expression products among, for example, normal and diseased cells, or among cells which have undergone different disease events or disease stages.
  • “individual” refers to a test subject or patient.
  • the individual can be a mammal or a non-mammal.
  • the individual is a mammal.
  • a mammalian individual can be a human or non-human.
  • the individual is a human.
  • a healthy or normal individual is an individual in which the disease or condition of interest (including, for example, Cardiovascular Events such as myocardial infarction, stroke and congestive heart failure) is not detectable by conventional diagnostic methods.
  • Diagnose”, “diagnosing”, “diagnosis”, and variations thereof refer to the detection, determination, or recognition of a health status or condition of an individual on the basis of one or more signs, symptoms, data, or other information pertaining to that individual.
  • the health status of an individual can be diagnosed as healthy/normal (i.e., a diagnosis of the absence of a disease or condition) or diagnosed as ill/abnormal (i.e., a diagnosis of the presence, or an assessment of the characteristics, of a disease or condition).
  • diagnosis encompass, with respect to a particular disease or condition, the initial detection of the disease; the characterization or classification of the disease; the detection of the progression, remission, or recurrence of the disease; and the detection of disease response after the administration of a treatment or therapy to the individual.
  • the prediction of risk of a CV event includes distinguishing individuals who have an increased risk of a CV event from individuals who do not.
  • Prognose refers to the prediction of a future course of a disease or condition in an individual who has the disease or condition (e.g., predicting patient survival), and such terms encompass the evaluation of disease or condition response after the administration of a treatment or therapy to the individual.
  • “Evaluate”, “evaluating”, “evaluation”, and variations thereof encompass both “diagnose” and “prognose” and also encompass determinations or predictions about the future course of a disease or condition in an individual who does not have the disease as well as determinations or predictions regarding the risk that a disease or condition will recur in an individual who apparently has been cured of the disease or has had the condition resolved.
  • the term “evaluate” also encompasses assessing an individual's response to a therapy, such as, for example, predicting whether an individual is likely to respond favorably to a therapeutic agent or is unlikely to respond to a therapeutic agent (or will experience toxic or other undesirable side effects, for example), selecting a therapeutic agent for administration to an individual, or monitoring or determining an individual's response to a therapy that has been administered to the individual.
  • “evaluating” risk of a CV vent can include, for example, any of the following: predicting the future risk of a CV event in an individual; predicting the risk of a CV event in an individual who apparently has no CV issues; or determining or predicting an individual's response to a CV treatment or selecting a CV treatment to administer to an individual based upon a determination of the biomarker values derived from the individual's biological sample.
  • Evaluation of risk of a CV event can include embodiments such as the assessment of risk of a CV event on a continuous scale, or classification of risk of a CV event in escalating classifications.
  • Classification of risk includes, for example, classification into two or more classifications such as “No Elevated Risk of a CV Event” and “Elevated Risk of a CV Event.”
  • the evaluation of risk of a CV event is for a defined period; such period can be, for example, 5 years.
  • additional biomedical information refers to one or more evaluations of an individual, other than using any of the biomarkers described herein, that are associated with CV risk or, more specifically, CV event risk.
  • “Additional biomedical information” includes any of the following: physical descriptors of an individual, including the height and/or weight of an individual; the age of an individual; the gender of an individual; change in weight; the ethnicity of an individual; occupational history; family history of cardiovascular disease (or other circulatory system disorders); the presence of a genetic marker(s) correlating with a higher risk of cardiovascular disease (or other circulatory system disorders) in the individual or a family member alterations in the carotid intima thickness; clinical symptoms such as chest pain, weight gain or loss gene expression values; physical descriptors of an individual, including physical descriptors observed by radiologic imaging; smoking status; alcohol use history; occupational history; dietary habits—salt, saturated fat and cholesterol intake; caffeine consumption; and imaging information such as electrocardiogram, echocardiography, carot
  • Biomarker levels in combination with an evaluation of any additional biomedical information may, for example, improve sensitivity, specificity, and/or AUC for prediction of CV events as compared to biomarker testing alone or evaluating any particular item of additional biomedical information alone (e.g., carotid intima thickness imaging alone).
  • Additional biomedical information can be obtained from an individual using routine techniques known in the art, such as from the individual themselves by use of a routine patient questionnaire or health history questionnaire, etc., or from a medical practitioner, etc.
  • Testing of biomarker levels in combination with an evaluation of any additional biomedical information may, for example, improve sensitivity, specificity, and/or thresholds for prediction of CV events (or other cardiovascular-related uses) as compared to biomarker testing alone or evaluating any particular item of additional biomedical information alone (e.g., CT imaging alone).
  • detecting or “determining” with respect to a biomarker value includes the use of both the instrument required to observe and record a signal corresponding to a biomarker value and the material/s required to generate that signal.
  • the biomarker value is detected using any suitable method, including fluorescence, chemiluminescence, surface plasmon resonance, surface acoustic waves, mass spectrometry, infrared spectroscopy, Raman spectroscopy, atomic force microscopy, scanning tunneling microscopy, electrochemical detection methods, nuclear magnetic resonance, quantum dots, and the like.
  • Solid support refers herein to any substrate having a surface to which molecules may be attached, directly or indirectly, through either covalent or non-covalent bonds.
  • a “solid support” can have a variety of physical formats, which can include, for example, a membrane; a chip (e.g., a protein chip); a slide (e.g., a glass slide or coverslip); a column; a hollow, solid, semi-solid, pore- or cavity-containing particle, such as, for example, a bead; a gel; a fiber, including a fiber optic material; a matrix; and a sample receptacle.
  • Exemplary sample receptacles include sample wells, tubes, capillaries, vials, and any other vessel, groove or indentation capable of holding a sample.
  • a sample receptacle can be contained on a multi-sample platform, such as a microtiter plate, slide, microfluidics device, and the like.
  • a support can be composed of a natural or synthetic material, an organic or inorganic material. The composition of the solid support on which capture reagents are attached generally depends on the method of attachment (e.g., covalent attachment).
  • Other exemplary receptacles include microdroplets and microfluidic controlled or bulk oil/aqueous emulsions within which assays and related manipulations can occur.
  • Suitable solid supports include, for example, plastics, resins, polysaccharides, silica or silica-based materials, functionalized glass, modified silicon, carbon, metals, inorganic glasses, membranes, nylon, natural fibers (such as, for example, silk, wool and cotton), polymers, and the like.
  • the material composing the solid support can include reactive groups such as, for example, carboxy, amino, or hydroxyl groups, which are used for attachment of the capture reagents.
  • Polymeric solid supports can include, e.g., polystyrene, polyethylene glycol tetraphthalate, polyvinyl acetate, polyvinyl chloride, polyvinyl pyrrolidone, polyacrylonitrile, polymethyl methacrylate, polytetrafluoroethylene, butyl rubber, styrenebutadiene rubber, natural rubber, polyethylene, polypropylene, (poly)tetrafluoroethylene, (poly)vinylidenefluoride, polycarbonate, and polymethylpentene.
  • Suitable solid support particles that can be used include, e.g., encoded particles, such as Luminex®-type encoded particles, magnetic particles, and glass particles.
  • methods for evaluating risk of a CV event in an individual by detecting one or more biomarker values corresponding to one or more biomarkers that are present in the circulation of an individual, such as in serum or plasma, by any number of analytical methods, including any of the analytical methods described herein.
  • biomarkers are, for example, differentially expressed in individuals with increased risk of a CV event as compared to individuals without increased risk of a CV event. Detection of the differential expression of a biomarker in an individual can be used, for example, to permit the prediction of risk of a CV event within 5 year time frame.
  • biomarker levels can also be done in conjunction with determination of SNPs or other genetic lesions or variability that are indicative of increased risk of susceptibility of disease or condition. (See, e.g., Amos et al., Nature Genetics 40, 616-622 (2009)).
  • biomarker levels can also be used in conjunction with radiologic screening. Biomarker levels can also be used in conjunction with relevant symptoms or genetic testing. Detection of any of the biomarkers described herein may be useful after the risk of CV event has been evaluated to guide appropriate clinical care of the individual, including increasing to more aggressive levels of care in high risk individuals after the CV event risk has been determined.
  • biomarkers can also be evaluated in conjunction with other types of data, particularly data that indicates an individual's risk for cardiovascular events (e.g., patient clinical history, symptoms, family history of cardiovascular disease, history of smoking or alcohol use, risk factors such as the presence of a genetic marker(s), and/or status of other biomarkers, etc.).
  • data that indicates an individual's risk for cardiovascular events (e.g., patient clinical history, symptoms, family history of cardiovascular disease, history of smoking or alcohol use, risk factors such as the presence of a genetic marker(s), and/or status of other biomarkers, etc.).
  • risk factors such as the presence of a genetic marker(s), and/or status of other biomarkers, etc.
  • biomarker levels in conjunction with radiologic screening in high risk individuals e.g., assessing biomarker levels in conjunction with blockage detected in a coronary angiogram
  • information regarding the biomarkers can also be evaluated in conjunction with other types of data, particularly data that indicates an individual's risk for having a CV event (e.g., patient clinical history, symptoms, family history of cardiovascular disease, risk factors such as whether or not the individual is a smoker, heavy alcohol user and/or status of other biomarkers, etc.).
  • data that indicates an individual's risk for having a CV event e.g., patient clinical history, symptoms, family history of cardiovascular disease, risk factors such as whether or not the individual is a smoker, heavy alcohol user and/or status of other biomarkers, etc.
  • CV event e.g., patient clinical history, symptoms, family history of cardiovascular disease, risk factors such as whether or not the individual is a smoker, heavy alcohol user and/or status of other biomarkers, etc.
  • the Framingham Risk score uses the following risk factors to result in a risk score: vascular tone, LDL-cholesterol and HDL-cholesterol levels, impaired glucose levels, smoking, systolic blood pressure, and diabetes.
  • the frequency of high-risk patients increases with age, and men comprise a greater proportion of high-risk patients than women.
  • an imaging agent can be coupled to any of the described biomarkers, which can be used to aid in prediction of risk of a Cardiovascular Event, to monitor reponse to therapeutic interventions, to select for target populations in a clinical trial among other uses.
  • a biomarker value for the biomarkers described herein can be detected using any of a variety of known analytical methods.
  • a biomarker value is detected using a capture reagent.
  • a “capture agent” or “capture reagent” refers to a molecule that is capable of binding specifically to a biomarker.
  • the capture reagent can be exposed to the biomarker in solution or can be exposed to the biomarker while the capture reagent is immobilized on a solid support.
  • the capture reagent contains a feature that is reactive with a secondary feature on a solid support.
  • the capture reagent can be exposed to the biomarker in solution, and then the feature on the capture reagent can be used in conjunction with the secondary feature on the solid support to immobilize the biomarker on the solid support.
  • the capture reagent is selected based on the type of analysis to be conducted.
  • Capture reagents include but are not limited to SOMAmers, antibodies, adnectins, ankyrins, other antibody mimetics and other protein scaffolds, autoantibodies, chimeras, small molecules, an F(ab′) 2 fragment, a single chain antibody fragment, an Fv fragment, a single chain Fv fragment, a nucleic acid, a lectin, a ligand-binding receptor, affybodies, nanobodies, imprinted polymers, avimers, peptidomimetics, a hormone receptor, a cytokine receptor, and synthetic receptors, and modifications and fragments of these.
  • a biomarker value is detected using a biomarker/capture reagent complex.
  • the biomarker value is derived from the biomarker/capture reagent complex and is detected indirectly, such as, for example, as a result of a reaction that is subsequent to the biomarker/capture reagent interaction, but is dependent on the formation of the biomarker/capture reagent complex.
  • the biomarker value is detected directly from the biomarker in a biological sample.
  • the biomarkers are detected using a multiplexed format that allows for the simultaneous detection of two or more biomarkers in a biological sample.
  • capture reagents are immobilized, directly or indirectly, covalently or non-covalently, in discrete locations on a solid support.
  • a multiplexed format uses discrete solid supports where each solid support has a unique capture reagent associated with that solid support, such as, for example quantum dots.
  • an individual device is used for the detection of each one of multiple biomarkers to be detected in a biological sample. Individual devices can be configured to permit each biomarker in the biological sample to be processed simultaneously. For example, a microtiter plate can be used such that each well in the plate is used to uniquely analyze one of multiple biomarkers to be detected in a biological sample.
  • a fluorescent tag can be used to label a component of the biomarker/capture complex to enable the detection of the biomarker value.
  • the fluorescent label can be conjugated to a capture reagent specific to any of the biomarkers described herein using known techniques, and the fluorescent label can then be used to detect the corresponding biomarker value.
  • Suitable fluorescent labels include rare earth chelates, fluorescein and its derivatives, rhodamine and its derivatives, dansyl, allophycocyanin, PBXL-3, Qdot 605, Lissamine, phycoerythrin, Texas Red, and other such compounds.
  • the fluorescent label is a fluorescent dye molecule.
  • the fluorescent dye molecule includes at least one substituted indolium ring system in which the substituent on the 3-carbon of the indolium ring contains a chemically reactive group or a conjugated substance.
  • the dye molecule includes an AlexFluor molecule, such as, for example, AlexaFluor 488, AlexaFluor 532, AlexaFluor 647, AlexaFluor 680, or AlexaFluor 700.
  • the dye molecule includes a first type and a second type of dye molecule, such as, e.g., two different AlexaFluor molecules.
  • the dye molecule includes a first type and a second type of dye molecule, and the two dye molecules have different emission spectra.
  • Fluorescence can be measured with a variety of instrumentation compatible with a wide range of assay formats.
  • spectrofluorimeters have been designed to analyze microtiter plates, microscope slides, printed arrays, cuvettes, etc. See Principles of Fluorescence Spectroscopy, by J. R. Lakowicz, Springer Science+Business Media, Inc., 2004. See Bioluminescence & Chemiluminescence: Progress & Current Applications; Philip E. Stanley and Larry J. Kricka editors, World Scientific Publishing Company, January 2002.
  • a chemiluminescence tag can optionally be used to label a component of the biomarker/capture complex to enable the detection of a biomarker value.
  • Suitable chemiluminescent materials include any of oxalyl chloride, Rodamin 6G, Ru(bipy)32+, TMAE (tetrakis(dimethylamino)ethylene), Pyrogallol (1,2,3-trihydroxibenzene), Lucigenin, peroxyoxalates, Aryl oxalates, Acridinium esters, dioxetanes, and others.
  • the detection method includes an enzyme/substrate combination that generates a detectable signal that corresponds to the biomarker value.
  • the enzyme catalyzes a chemical alteration of the chromogenic substrate which can be measured using various techniques, including spectrophotometry, fluorescence, and chemiluminescence.
  • Suitable enzymes include, for example, luciferases, luciferin, malate dehydrogenase, urease, horseradish peroxidase (HRPO), alkaline phosphatase, beta-galactosidase, glucoamylase, lysozyme, glucose oxidase, galactose oxidase, and glucose-6-phosphate dehydrogenase, uricase, xanthine oxidase, lactoperoxidase, microperoxidase, and the like.
  • HRPO horseradish peroxidase
  • alkaline phosphatase beta-galactosidase
  • glucoamylase lysozyme
  • glucose oxidase galactose oxidase
  • glucose-6-phosphate dehydrogenase uricase
  • xanthine oxidase lactoperoxidase
  • microperoxidase and the like.
  • the detection method can be a combination of fluorescence, chemiluminescence, radionuclide or enzyme/substrate combinations that generate a measurable signal.
  • Multimodal signaling could have unique and advantageous characteristics in biomarker assay formats.
  • biomarker values for the biomarkers described herein can be detected using known analytical methods including, singleplex SOMAmer assays, multiplexed SOMAmer assays, singleplex or multiplexed immunoassays, mRNA expression profiling, miRNA expression profiling, mass spectrometric analysis, histological/cytological methods, etc. as detailed below.
  • Assays directed to the detection and quantification of physiologically significant molecules in biological samples and other samples are important tools in scientific research and in the health care field.
  • One class of such assays involves the use of a microarray that includes one or more aptamers immobilized on a solid support.
  • the aptamers are each capable of binding to a target molecule in a highly specific manner and with very high affinity. See, e.g., U.S. Pat. No. 5,475,096 entitled “Nucleic Acid Ligands”; see also, e.g., U.S. Pat. No. 6,242,246, U.S. Pat. No. 6,458,543, and U.S. Pat. No.
  • an “aptamer” refers to a nucleic acid that has a specific binding affinity for a target molecule. It is recognized that affinity interactions are a matter of degree; however, in this context, the “specific binding affinity” of an aptamer for its target means that the aptamer binds to its target generally with a much higher degree of affinity than it binds to other components in a test sample.
  • An “aptamer” is a set of copies of one type or species of nucleic acid molecule that has a particular nucleotide sequence.
  • An aptamer can include any suitable number of nucleotides, including any number of chemically modified nucleotides. “Aptamers” refers to more than one such set of molecules.
  • aptamers can have either the same or different numbers of nucleotides.
  • Aptamers can be DNA or RNA or chemically modified nucleic acids and can be single stranded, double stranded, or contain double stranded regions, and can include higher ordered structures.
  • An aptamer can also be a photoaptamer, where a photoreactive or chemically reactive functional group is included in the aptamer to allow it to be covalently linked to its corresponding target. Any of the aptamer methods disclosed herein can include the use of two or more aptamers that specifically bind the same target molecule.
  • an aptamer may include a tag. If an aptamer includes a tag, all copies of the aptamer need not have the same tag. Moreover, if different aptamers each include a tag, these different aptamers can have either the same tag or a different tag.
  • An aptamer can be identified using any known method, including the SELEX process. Once identified, an aptamer can be prepared or synthesized in accordance with any known method, including chemical synthetic methods and enzymatic synthetic methods.
  • a “SOMAmer” or Slow Off-Rate Modified Aptamer refers to an aptamer having improved off-rate characteristics. SOMAmers can be generated using the improved SELEX methods described in U.S. Publication No. 2009/0004667, entitled “Method for Generating Aptamers with Improved Off-Rates.”
  • SELEX and “SELEX process” are used interchangeably herein to refer generally to a combination of (1) the selection of aptamers that interact with a target molecule in a desirable manner, for example binding with high affinity to a protein, with (2) the amplification of those selected nucleic acids.
  • the SELEX process can be used to identify aptamers with high affinity to a specific target or biomarker.
  • SELEX generally includes preparing a candidate mixture of nucleic acids, binding of the candidate mixture to the desired target molecule to form an affinity complex, separating the affinity complexes from the unbound candidate nucleic acids, separating and isolating the nucleic acid from the affinity complex, purifying the nucleic acid, and identifying a specific aptamer sequence.
  • the process may include multiple rounds to further refine the affinity of the selected aptamer.
  • the process can include amplification steps at one or more points in the process. See, e.g., U.S. Pat. No. 5,475,096, entitled “Nucleic Acid Ligands”.
  • the SELEX process can be used to generate an aptamer that covalently binds its target as well as an aptamer that non-covalently binds its target. See, e.g., U.S. Pat. No. 5,705,337 entitled “Systematic Evolution of Nucleic Acid Ligands by Exponential Enrichment: Chemi-SELEX.”
  • the SELEX process can be used to identify high-affinity aptamers containing modified nucleotides that confer improved characteristics on the aptamer, such as, for example, improved in vivo stability or improved delivery characteristics. Examples of such modifications include chemical substitutions at the ribose and/or phosphate and/or base positions. SELEX process-identified aptamers containing modified nucleotides are described in U.S. Pat. No. 5,660,985, entitled “High Affinity Nucleic Acid Ligands Containing Modified Nucleotides”, which describes oligonucleotides containing nucleotide derivatives chemically modified at the 5′- and 2′-positions of pyrimidines. U.S. Pat. No.
  • SELEX can also be used to identify aptamers that have desirable off-rate characteristics. See U.S. Patent Application Publication 20090004667, entitled “Method for Generating Aptamers with Improved Off-Rates”, which describes improved SELEX methods for generating aptamers that can bind to target molecules. As mentioned above, these slow off-rate aptamers are known as “SOMAmers.” Methods for producing aptamers or SOMAmers and photoaptamers or SOMAmers having slower rates of dissociation from their respective target molecules are described.
  • the methods involve contacting the candidate mixture with the target molecule, allowing the formation of nucleic acid-target complexes to occur, and performing a slow off-rate enrichment process wherein nucleic acid-target complexes with fast dissociation rates will dissociate and not reform, while complexes with slow dissociation rates will remain intact. Additionally, the methods include the use of modified nucleotides in the production of candidate nucleic acid mixtures to generate aptamers or SOMAmers with improved off-rate performance.
  • a variation of this assay employs aptamers that include photoreactive functional groups that enable the aptamers to covalently bind or “photocrosslink” their target molecules. See, e.g., U.S. Pat. No. 6,544,776 entitled “Nucleic Acid Ligand Diagnostic Biochip”. These photoreactive aptamers are also referred to as photoaptamers. See, e.g., U.S. Pat. No. 5,763,177, U.S. Pat. No. 6,001,577, and U.S. Pat. No.
  • Harsh wash conditions may be used, since target molecules that are bound to the photoaptamers are generally not removed, due to the covalent bonds created by the photoactivated functional group(s) on the photoaptamers.
  • the assay enables the detection of a biomarker value corresponding to a biomarker in the test sample.
  • the aptamers or SOMAmers are immobilized on the solid support prior to being contacted with the sample.
  • immobilization of the aptamers or SOMAmers prior to contact with the sample may not provide an optimal assay.
  • pre-immobilization of the aptamers or SOMAmers may result in inefficient mixing of the aptamers or SOMAmers with the target molecules on the surface of the solid support, perhaps leading to lengthy reaction times and, therefore, extended incubation periods to permit efficient binding of the aptamers or SOMAmers to their target molecules.
  • the solid support may tend to scatter or absorb the light used to effect the formation of covalent bonds between the photoaptamers or photoSOMAmers and their target molecules.
  • detection of target molecules bound to their aptamers or photoSOMAmers can be subject to imprecision, since the surface of the solid support may also be exposed to and affected by any labeling agents that are used.
  • immobilization of the aptamers or SOMAmers on the solid support generally involves an aptamer or SOMAmer-preparation step (i.e., the immobilization) prior to exposure of the aptamers or SOMAmers to the sample, and this preparation step may affect the activity or functionality of the aptamers or SOMAmers.
  • SOMAmer assays that permit a SOMAmer to capture its target in solution and then employ separation steps that are designed to remove specific components of the SOMAmer-target mixture prior to detection have also been described (see U.S. Patent Application Publication 20090042206, entitled “Multiplexed Analyses of Test Samples”).
  • the described SOMAmer assay methods enable the detection and quantification of a non-nucleic acid target (e.g., a protein target) in a test sample by detecting and quantifying a nucleic acid (i.e., a SOMAmer).
  • the described methods create a nucleic acid surrogate (i.e., the SOMAmer) for detecting and quantifying a non-nucleic acid target, thus allowing the wide variety of nucleic acid technologies, including amplification, to be applied to a broader range of desired targets, including protein targets.
  • a nucleic acid surrogate i.e., the SOMAmer
  • SOMAmers can be constructed to facilitate the separation of the assay components from a SOMAmer biomarker complex (or photoSOMAmer biomarker covalent complex) and permit isolation of the SOMAmer for detection and/or quantification.
  • these constructs can include a cleavable or releasable element within the SOMAmer sequence.
  • additional functionality can be introduced into the SOMAmer, for example, a labeled or detectable component, a spacer component, or a specific binding tag or immobilization element.
  • the SOMAmer can include a tag connected to the SOMAmer via a cleavable moiety, a label, a spacer component separating the label, and the cleavable moiety.
  • a cleavable element is a photocleavable linker.
  • the photocleavable linker can be attached to a biotin moiety and a spacer section, can include an NHS group for derivatization of amines, and can be used to introduce a biotin group to a SOMAmer, thereby allowing for the release of the SOMAmer later in an assay method.
  • a method for signal generation takes advantage of anisotropy signal change due to the interaction of a fluorophore-labeled capture reagent with its specific biomarker target.
  • the labeled capture reagent reacts with its target, the increased molecular weight causes the rotational motion of the fluorophore attached to the complex to become much slower changing the anisotropy value.
  • binding events may be used to quantitatively measure the biomarkers in solutions.
  • Other methods include fluorescence polarization assays, molecular beacon methods, time resolved fluorescence quenching, chemiluminescence, fluorescence resonance energy transfer, and the like.
  • An exemplary solution-based SOMAmer assay that can be used to detect a biomarker value corresponding to a biomarker in a biological sample includes the following: (a) preparing a mixture by contacting the biological sample with a SOMAmer that includes a first tag and has a specific affinity for the biomarker, wherein a SOMAmer affinity complex is formed when the biomarker is present in the sample; (b) exposing the mixture to a first solid support including a first capture element, and allowing the first tag to associate with the first capture element; (c) removing any components of the mixture not associated with the first solid support; (d) attaching a second tag to the biomarker component of the SOMAmer affinity complex; (e) releasing the SOMAmer affinity complex from the first solid support; (f) exposing the released SOMAmer affinity complex to a second solid support that includes a second capture element and allowing the second tag to associate with the second capture element; (g) removing any non-complexed SOMAmer from the mixture by partitioning the non
  • any means known in the art can be used to detect a biomarker value by detecting the SOMAmer component of a SOMAmer affinity complex.
  • a number of different detection methods can be used to detect the SOMAmer component of an affinity complex, such as, for example, hybridization assays, mass spectroscopy, or QPCR.
  • nucleic acid sequencing methods can be used to detect the SOMAmer component of a SOMAmer affinity complex and thereby detect a biomarker value. Briefly, a test sample can be subjected to any kind of nucleic acid sequencing method to identify and quantify the sequence or sequences of one or more SOMAmers present in the test sample.
  • the sequence includes the entire SOMAmer molecule or any portion of the molecule that may be used to uniquely identify the molecule.
  • the identifying sequencing is a specific sequence added to the SOMAmer; such sequences are often referred to as “tags,” “barcodes,” or “zipcodes.”
  • the sequencing method includes enzymatic steps to amplify the SOMAmer sequence or to convert any kind of nucleic acid, including RNA and DNA that contain chemical modifications to any position, to any other kind of nucleic acid appropriate for sequencing.
  • the sequencing method includes one or more cloning steps. In other embodiments the sequencing method includes a direct sequencing method without cloning.
  • the sequencing method includes a directed approach with specific primers that target one or more SOMAmers in the test sample. In other embodiments, the sequencing method includes a shotgun approach that targets all SOMAmers in the test sample.
  • the sequencing method includes enzymatic steps to amplify the molecule targeted for sequencing. In other embodiments, the sequencing method directly sequences single molecules.
  • An exemplary nucleic acid sequencing-based method that can be used to detect a biomarker value corresponding to a biomarker in a biological sample includes the following: (a) converting a mixture of SOMAmers that contain chemically modified nucleotides to unmodified nucleic acids with an enzymatic step; (b) shotgun sequencing the resulting unmodified nucleic acids with a massively parallel sequencing platform such as, for example, the 454 Sequencing System (454 Life Sciences/Roche), the Illumina Sequencing System (Illumina), the ABI SOLiD Sequencing System (Applied Biosystems), the HeliScope Single Molecule Sequencer (Helicos Biosciences), or the Pacific Biosciences Real Time Single-Molecule Sequencing System (Pacific BioSciences) or the Polonator G Sequencing System (Dover Systems); and
  • Immunoassay methods are based on the reaction of an antibody to its corresponding target or analyte and can detect the analyte in a sample depending on the specific assay format.
  • monoclonal antibodies are often used because of their specific epitope recognition.
  • Polyclonal antibodies have also been successfully used in various immunoassays because of their increased affinity for the target as compared to monoclonal antibodies.
  • Immunoassays have been designed for use with a wide range of biological sample matrices. Immunoassay formats have been designed to provide qualitative, semi-quantitative, and quantitative results.
  • Quantitative results are generated through the use of a standard curve created with known concentrations of the specific analyte to be detected.
  • the response or signal from an unknown sample is plotted onto the standard curve, and a quantity or value corresponding to the target in the unknown sample is established.
  • ELISA or EIA can be quantitative for the detection of an analyte. This method relies on attachment of a label to either the analyte or the antibody and the label component includes, either directly or indirectly, an enzyme. ELISA tests may be formatted for direct, indirect, competitive, or sandwich detection of the analyte. Other methods rely on labels such as, for example, radioisotopes (1125) or fluorescence.
  • Additional techniques include, for example, agglutination, nephelometry, turbidimetry, Western blot, immunoprecipitation, immunocytochemistry, immunohistochemistry, flow cytometry, Luminex assay, and others (see ImmunoAs say: A Practical Guide, edited by Brian Law, published by Taylor & Francis, Ltd., 2005 edition).
  • Exemplary assay formats include enzyme-linked immunosorbent assay (ELISA), radioimmunoassay, fluorescent, chemiluminescence, and fluorescence resonance energy transfer (FRET) or time resolved-FRET (TR-FRET) immunoassays.
  • ELISA enzyme-linked immunosorbent assay
  • FRET fluorescence resonance energy transfer
  • TR-FRET time resolved-FRET
  • biomarkers include biomarker immunoprecipitation followed by quantitative methods that allow size and peptide level discrimination, such as gel electrophoresis, capillary electrophoresis, planar electrochromatography, and the like.
  • Methods of detecting and/or quantifying a detectable label or signal generating material depend on the nature of the label.
  • the products of reactions catalyzed by appropriate enzymes can be, without limitation, fluorescent, luminescent, or radioactive or they may absorb visible or ultraviolet light.
  • detectors suitable for detecting such detectable labels include, without limitation, x-ray film, radioactivity counters, scintillation counters, spectrophotometers, colorimeters, fluorometers, luminometers, and densitometers.
  • Any of the methods for detection can be performed in any format that allows for any suitable preparation, processing, and analysis of the reactions. This can be, for example, in multi-well assay plates (e.g., 96 wells or 384 wells) or using any suitable array or microarray. Stock solutions for various agents can be made manually or robotically, and all subsequent pipetting, diluting, mixing, distribution, washing, incubating, sample readout, data collection and analysis can be done robotically using commercially available analysis software, robotics, and detection instrumentation capable of detecting a detectable label.
  • Measuring mRNA in a biological sample may be used as a surrogate for detection of the level of the corresponding protein in the biological sample.
  • any of the biomarkers or biomarker panels described herein can also be detected by detecting the appropriate RNA.
  • mRNA expression levels are measured by reverse transcription quantitative polymerase chain reaction (RT-PCR followed with qPCR).
  • RT-PCR is used to create a cDNA from the mRNA.
  • the cDNA may be used in a qPCR assay to produce fluorescence as the DNA amplification process progresses. By comparison to a standard curve, qPCR can produce an absolute measurement such as number of copies of mRNA per cell.
  • Northern blots, microarrays, Invader assays, and RT-PCR combined with capillary electrophoresis have all been used to measure expression levels of mRNA in a sample. See Gene Expression Profiling: Methods and Protocols, Richard A. Shimkets, editor, Humana Press, 2004.
  • miRNA molecules are small RNAs that are non-coding but may regulate gene expression. Any of the methods suited to the measurement of mRNA expression levels can also be used for the corresponding miRNA. Recently many laboratories have investigated the use of miRNAs as biomarkers for disease. Many diseases involve wide-spread transcriptional regulation, and it is not surprising that miRNAs might find a role as biomarkers. The connection between miRNA concentrations and disease is often even less clear than the connections between protein levels and disease, yet the value of miRNA biomarkers might be substantial.
  • RNA biomarkers have similar requirements, although many potential protein biomarkers are secreted intentionally at the site of pathology and function, during disease, in a paracrine fashion. Many potential protein biomarkers are designed to function outside the cells within which those proteins are synthesized.
  • any of the described biomarkers may also be used in molecular imaging tests.
  • an imaging agent can be coupled to any of the described biomarkers, which can be used to aid in prediction of risk of Cardiovascular Events within 5 years, to monitor response to therapeutic interventions, to select a population for clinical trials among other uses.
  • In vivo imaging technologies provide non-invasive methods for determining the state of a particular disease or condition in the body of an individual. For example, entire portions of the body, or even the entire body, may be viewed as a three dimensional image, thereby providing valuable information concerning morphology and structures in the body. Such technologies may be combined with the detection of the biomarkers described herein to provide information concerning the cardiovascular status of an individual.
  • in vivo molecular imaging technologies are expanding due to various advances in technology. These advances include the development of new contrast agents or labels, such as radiolabels and/or fluorescent labels, which can provide strong signals within the body; and the development of powerful new imaging technology, which can detect and analyze these signals from outside the body, with sufficient sensitivity and accuracy to provide useful information.
  • the contrast agent can be visualized in an appropriate imaging system, thereby providing an image of the portion or portions of the body in which the contrast agent is located.
  • the contrast agent may be bound to or associated with a capture reagent, such as a SOMAmer or an antibody, for example, and/or with a peptide or protein, or an oligonucleotide (for example, for the detection of gene expression), or a complex containing any of these with one or more macromolecules and/or other particulate forms.
  • a capture reagent such as a SOMAmer or an antibody, for example, and/or with a peptide or protein, or an oligonucleotide (for example, for the detection of gene expression), or a complex containing any of these with one or more macromolecules and/or other particulate forms.
  • the contrast agent may also feature a radioactive atom that is useful in imaging.
  • Suitable radioactive atoms include technetium-99m or iodine-123 for scintigraphic studies.
  • Other readily detectable moieties include, for example, spin labels for magnetic resonance imaging (MRI) such as, for example, iodine-123 again, iodine-131, indium-111, fluorine-19, carbon-13, nitrogen-15, oxygen-17, gadolinium, manganese or iron.
  • MRI magnetic resonance imaging
  • Standard imaging techniques include but are not limited to magnetic resonance imaging, computed tomography scanning (coronary calcium score), positron emission tomography (PET), single photon emission computed tomography (SPECT), computed tomography angiography, and the like.
  • a given contrast agent such as a given radionuclide and the particular biomarker that it is used to target (protein, mRNA, and the like).
  • the radionuclide chosen typically has a type of decay that is detectable by a given type of instrument.
  • its half-life should be long enough to enable detection at the time of maximum uptake by the target tissue but short enough that deleterious radiation of the host is minimized.
  • Exemplary imaging techniques include but are not limited to PET and SPECT, which are imaging techniques in which a radionuclide is synthetically or locally administered to an individual. The subsequent uptake of the radiotracer is measured over time and used to obtain information about the targeted tissue and the biomarker. Because of the high-energy (gamma-ray) emissions of the specific isotopes employed and the sensitivity and sophistication of the instruments used to detect them, the two-dimensional distribution of radioactivity may be inferred from outside of the body.
  • PET and SPECT are imaging techniques in which a radionuclide is synthetically or locally administered to an individual. The subsequent uptake of the radiotracer is measured over time and used to obtain information about the targeted tissue and the biomarker. Because of the high-energy (gamma-ray) emissions of the specific isotopes employed and the sensitivity and sophistication of the instruments used to detect them, the two-dimensional distribution of radioactivity may be inferred from outside of the body.
  • positron-emitting nuclides in PET include, for example, carbon-11, nitrogen-13, oxygen-15, and fluorine-18.
  • Isotopes that decay by electron capture and/or gamma-emission are used in SPECT and include, for example iodine-123 and technetium-99m.
  • An exemplary method for labeling amino acids with technetium-99m is the reduction of pertechnetate ion in the presence of a chelating precursor to form the labile technetium-99m-precursor complex, which, in turn, reacts with the metal binding group of a bifunctionally modified chemotactic peptide to form a technetium-99m-chemotactic peptide conjugate.
  • Antibodies are frequently used for such in vivo imaging diagnostic methods.
  • the preparation and use of antibodies for in vivo diagnosis is well known in the art.
  • Labeled antibodies which specifically bind any of the biomarkers in Table 1, Col. 7 can be injected into an individual suspected of having an increased risk of a CV event, detectable according to the particular biomarker used, for the purpose of diagnosing or evaluating the disease status or condition of the individual.
  • the label used will be selected in accordance with the imaging modality to be used, as previously described. Localization of the label permits determination of the tissue damage or other indications related to the risk of a CV event.
  • the amount of label within an organ or tissue also allows determination of the involvement of the CV event biomarkers due to the risk of a CV event in that organ or tissue.
  • SOMAmers may be used for such in vivo imaging diagnostic methods.
  • a SOMAmer that was used to identify a particular biomarker described in Table 1, Col. 7 (and therefore binds specifically to that particular biomarker) may be appropriately labeled and injected into an individual suspected of having had a CV event, detectable according to the particular biomarker, for the purpose of diagnosing or evaluating the levels of tissue damage, atherosclerotic plaques, components of inflammatory response and other factors associated with the risk of a CV event in the individual.
  • the label used will be selected in accordance with the imaging modality to be used, as previously described. Localization of the label permits determination of the site of the processes leading to increased risk.
  • SOMAmer-directed imaging agents could have unique and advantageous characteristics relating to tissue penetration, tissue distribution, kinetics, elimination, potency, and selectivity as compared to other imaging agents.
  • Such techniques may also optionally be performed with labeled oligonucleotides, for example, for detection of gene expression through imaging with antisense oligonucleotides. These methods are used for in situ hybridization, for example, with fluorescent molecules or radionuclides as the label. Other methods for detection of gene expression include, for example, detection of the activity of a reporter gene.
  • optical imaging Another general type of imaging technology is optical imaging, in which fluorescent signals within the subject are detected by an optical device that is external to the subject. These signals may be due to actual fluorescence and/or to bioluminescence. Improvements in the sensitivity of optical detection devices have increased the usefulness of optical imaging for in vivo diagnostic assays.
  • in vivo molecular biomarker imaging is increasing, including for clinical trials, for example, to more rapidly measure clinical efficacy in trials for new disease or condition therapies and/or to avoid prolonged treatment with a placebo for those diseases, such as multiple sclerosis, in which such prolonged treatment may be considered to be ethically questionable.
  • mass spectrometers can be used to detect biomarker values.
  • Several types of mass spectrometers are available or can be produced with various configurations.
  • a mass spectrometer has the following major components: a sample inlet, an ion source, a mass analyzer, a detector, a vacuum system, and instrument-control system, and a data system. Difference in the sample inlet, ion source, and mass analyzer generally define the type of instrument and its capabilities.
  • an inlet can be a capillary-column liquid chromatography source or can be a direct probe or stage such as used in matrix-assisted laser desorption.
  • Common ion sources are, for example, electrospray, including nanospray and microspray or matrix-assisted laser desorption.
  • Common mass analyzers include a quadrupole mass filter, ion trap mass analyzer and time-of-flight mass analyzer. Additional mass spectrometry methods are well known in the art (see Burlingame et al. Anal. Chem. 70:647 R-716R (1998); Kinter and Sherman, N.Y. (2000)).
  • Protein biomarkers and biomarker values can be detected and measured by any of the following: electrospray ionization mass spectrometry (ESI-MS), ESI-MS/MS, ESI-MS/(MS)n, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS), surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS), desorption/ionization on silicon (DIOS), secondary ion mass spectrometry (SIMS), quadrupole time-of-flight (Q-TOF), tandem time-of-flight (TOF/TOF) technology, called ultraflex III TOF/TOF, atmospheric pressure chemical ionization mass spectrometry (APCI-MS), APCI-MS/MS, APCI-(MS)N, atmospheric pressure photoionization mass spectrometry (APPI-MS), APPI-MS/MS,
  • Labeling methods include but are not limited to isobaric tag for relative and absolute quantitation (iTRAQ) and stable isotope labeling with amino acids in cell culture (SILAC).
  • Capture reagents used to selectively enrich samples for candidate biomarker proteins prior to mass spectroscopic analysis include but are not limited to SOMAmers, antibodies, nucleic acid probes, chimeras, small molecules, an F(ab′)2 fragment, a single chain antibody fragment, an Fv fragment, a single chain Fv fragment, a nucleic acid, a lectin, a ligand-binding receptor, affybodies, nanobodies, ankyrins, domain antibodies, alternative antibody scaffolds (e.g.
  • a proximity ligation assay can be used to determine biomarker values. Briefly, a test sample is contacted with a pair of affinity probes that may be a pair of antibodies or a pair of SOMAmers, with each member of the pair extended with an oligonucleotide.
  • the targets for the pair of affinity probes may be two distinct determinates on one protein or one determinate on each of two different proteins, which may exist as homo- or hetero-multimeric complexes. When probes bind to the target determinates, the free ends of the oligonucleotide extensions are brought into sufficiently close proximity to hybridize together.
  • oligonucleotide extensions The hybridization of the oligonucleotide extensions is facilitated by a common connector oligonucleotide which serves to bridge together the oligonucleotide extensions when they are positioned in sufficient proximity. Once the oligonucleotide extensions of the probes are hybridized, the ends of the extensions are joined together by enzymatic DNA ligation.
  • Each oligonucleotide extension comprises a primer site for PCR amplification.
  • the oligonucleotides form a continuous DNA sequence which, through PCR amplification, reveals information regarding the identity and amount of the target protein, as well as, information regarding protein-protein interactions where the target determinates are on two different proteins.
  • Proximity ligation can provide a highly sensitive and specific assay for real-time protein concentration and interaction information through use of real-time PCR. Probes that do not bind the determinates of interest do not have the corresponding oligonucleotide extensions brought into proximity and no ligation or PCR amplification can proceed, resulting in no signal being produced.
  • the foregoing assays enable the detection of biomarker values that are useful in methods for prediction of risk of CV events, where the methods comprise detecting, in a biological sample from an individual, at least N biomarker values that each correspond to a biomarker selected from the group consisting of the biomarkers provided in Table 1, Col. 7, wherein a classification, as described in detail below, using the biomarker values indicates whether the individual has elevated risk of a CV event occurring within a 5 year time period. While certain of the described CV event biomarkers are useful alone for predicting risk of a CV event, methods are also described herein for the grouping of multiple subsets of the CV event biomarkers that are each useful as a panel of three or more biomarkers.
  • N is at least three biomarkers.
  • N is selected to be any number from 2-155 biomarkers. It will be appreciated that N can be selected to be any number from any of the above described ranges, as well as similar, but higher order, ranges.
  • biomarker values can be detected and classified individually or they can be detected and classified collectively, as for example in a multiplex assay format.
  • a biomarker “signature” for a given diagnostic or predictive test contains a set of markers, each marker having different levels in the populations of interest. Different levels, in this context, may refer to different means of the marker levels for the individuals in two or more groups, or different variances in the two or more groups, or a combination of both. For the simplest form of a diagnostic test, these markers can be used to assign an unknown sample from an individual into one of two groups, either at increased risk of a CV event or not. The assignment of a sample into one of two or more groups is known as classification, and the procedure used to accomplish this assignment is known as a classifier or a classification method. Classification methods may also be referred to as scoring methods.
  • classification methods There are many classification methods that can be used to construct a diagnostic classifier from a set of biomarker values.
  • classification methods are most easily performed using supervised learning techniques where a data set is collected using samples obtained from individuals within two (or more, for multiple classification states) distinct groups one wishes to distinguish. Since the class (group or population) to which each sample belongs is known in advance for each sample, the classification method can be trained to give the desired classification response. It is also possible to use unsupervised learning techniques to produce a diagnostic classifier.
  • diagnostic classifiers include decision trees; bagging, boosting, forests and random forests; rule inference based learning; Parzen Windows; linear models; logistic; neural network methods; unsupervised clustering; K-means; hierarchical ascending/descending; semi-supervised learning; prototype methods; nearest neighbor; kernel density estimation; support vector machines; hidden Markov models; Boltzmann Learning; and classifiers may be combined either simply or in ways which minimize particular objective functions.
  • Pattern Classification R. O. Duda, et al., editors, John Wiley & Sons, 2nd edition, 2001
  • the Elements of Statistical Learning—Data Mining, Inference, and Prediction T. Hastie, et al., editors, Springer Science+Business Media, LLC, 2nd edition, 2009; each of which is incorporated by reference in its entirety.
  • training data includes samples from the distinct groups (classes) to which unknown samples will later be assigned.
  • samples collected from individuals in a control population and individuals in a particular disease, condition or event population can constitute training data to develop a classifier that can classify unknown samples (or, more particularly, the individuals from whom the samples were obtained) as either having the disease, condition or elevated risk of an event or being free from the disease, condition or elevated risk of an event.
  • the development of the classifier from the training data is known as training the classifier. Specific details on classifier training depend on the nature of the supervised learning technique (see, e.g., Pattern Classification, R. O.
  • Over-fitting occurs when a statistical model describes random error or noise instead of the underlying relationship. Over-fitting can be avoided in a variety of ways, including, for example, by limiting the number of markers used in developing the classifier, by assuming that the marker responses are independent of one another, by limiting the complexity of the underlying statistical model employed, and by ensuring that the underlying statistical model conforms to the data.
  • PCA Principal Component Analysis
  • biomarkers can be analyzed for those components of difference between samples which were specific to the separation between the control samples and early event samples.
  • One method that may be employed is the use of DSGA (Bair, E. and Tibshirani, R. (2004) Semi-supervised methods to predict patient survival from gene expression data. PLOS Biol., 2, 511-522) to remove (deflate) the first three principal component directions of variation between the samples in the control set.
  • DSGA Air, E. and Tibshirani, R. (2004) Semi-supervised methods to predict patient survival from gene expression data. PLOS Biol., 2, 511-522) to remove (deflate) the first three principal component directions of variation between the samples in the control set.
  • the dimensionality reduction is performed on the control set to discover, both the samples in the control and the samples from the early event samples are run through the PCA. Separation of cases from early events can be observed along the horizontal axis ( FIG. 2B ).
  • Cross-validation involves the multiple selection of sets of samples to determine the association of risk by protein combined with the use of the unselected samples to monitor the ability of the method to apply to samples which were not used in producing the model of risk (The Elements of Statistical Learning—Data Mining, Inference, and Prediction, T. Hastie, et al., editors, Springer Science+Business Media, LLC, 2nd edition, 2009).
  • We applied the supervised PCA method of Tibshirani et al (Bair, E. and Tibshirani, R. (2004) Semi-supervised methods to predict patient survival from gene expression data.
  • the supervised PCA (SPCA) method inolves the univariate selection of a set of proteins statistically associated with the observed event hazard in the data and the determination of the correlated component which combines information from all of these proteins. This determination of the correlated component is a dimensionality reduction step which not only combines information across proteins, but also mitigates the likelihood of overfitting by reducing the number of independent variables from the full protein menu of over 1000 proteins down to a few principal components (in this work, we only examined the first principal component).
  • Tibshirani et al is especially protected against false discovery by the use of prevalidation method of cross-validation and the dimensional reduction inherent in PCA.
  • the list of 155 proteins from SPCA was used to check subsequent analyses using different techniques to detect the false discovery of protein markers, not contained in the list of 155 proteins from SPCA.
  • Cox proportional hazard model (Cox, David R (1972). “Regression Models and Life-Tables”. Journal of the Royal Statistical Society. Series B (Methodological) 34 (2): 187-220.)) is widely used in medical statistics. Cox regression avoids fitting a specific function of time to the cumulative survival, and instead employs a model of relative risk referred to a baseline hazard function (which may vary with time).
  • the baseline hazard function describes the common shape of the survival time distribution for all individuals, while the relative risk gives the level of the hazard for a set of covariate values (such as a single individual or group), as a multiple of the baseline hazard.
  • the relative risk is constant with time in the Cox model.
  • GFR GFR Decreases in GFR will increase all proteins with non-zero renal clearance, the concentration of a protein in the blood is reduced through loss of the protein into the urine via the kidney (clearance), reduced renal filtration as measured by GFR is thus associated with increased concentration of those proteins in the blood which are partially filtered by the kidney.
  • the next step filtered the 20 proteins down to ten by requiring that the p-value should be more significant than 0.01. This step suppresses covariant proteins and allows independent proteins to contribute.
  • a final adjustment was made to the biomarker selection in that C9, a member of the membrane attack complex in the final common pathway of the complement system, was judged to be too unspecific in its signaling, a matter which cannot be decided from this study alone, since the study is created to cleanly demonstrate Cardio vascular risk.
  • C9 was removed and all the remaining proteins were evaluated in its place. The substitute proteins were ranked on the improvement in the Wald test score, and KLK3.SerpinA3 was close to as effective as C9.
  • the Kaplan Meier survival curves are shown in FIGS. 3A-3E for this ten marker model of cardiovascular risk.
  • Table 1 identifies 155 biomarkers that are useful for evaluation the risk of a future CV event in an individual. This is a surprisingly larger number than expected when compared to what is typically found during biomarker discovery efforts and may be attributable to the scale of the described study, which encompassed over 1000 proteins measured in hundreds of individual samples, in some cases at concentrations in the low femtomolar range.
  • the large number of discovered biomarkers reflects the diverse biochemical pathways implicated in the biology leading up to a cardiovascular event and the body's response to the CV event; each pathway and process involves many proteins. The results show that no single protein of a small group of proteins is uniquely informative about such complex processes; rather, that multiple proteins are involved in relevant processes, such as GFR, atherosclerosis, inflammation and hormonal CV regulation, for example.
  • Example 2 The results from Example 2 suggest certain possible conclusions: First, the identification of a large number of biomarkers enables their aggregation into a vast number of classifiers that offer similarly high performance. Second, classifiers can be constructed such that particular biomarkers may be substituted for other biomarkers in a manner that reflects the redundancies that undoubtedly pervade the complexities of the underlying disease, condition or event processes. That is to say, the information about the disease, condition or event contributed by any individual biomarker identified in Table 1 overlaps with the information contributed by other biomarkers, such that it may be that no particular biomarker or small group of biomarkers in Table 1 must be included in any classifier.
  • any combination of the biomarkers of Table 1, Col. 7 can be detected using a suitable kit, such as for use in performing the methods disclosed herein.
  • a suitable kit such as for use in performing the methods disclosed herein.
  • any kit can contain one or more detectable labels as described herein, such as a fluorescent moiety, etc.
  • a kit in one embodiment, includes (a) one or more capture reagents (such as, for example, at least one SOMAmer or antibody) for detecting one or more biomarkers in a biological sample, wherein the biomarkers include any of the biomarkers set forth in Table 1, Col. 7, and optionally (b) one or more software or computer program products for classifying the individual from whom the biological sample was obtained as either having or not having increased risk of a CV event or for determining the likelihood that the individual has increased risk of a CV event, as further described herein.
  • one or more instructions for manually performing the above steps by a human can be provided.
  • kit The combination of a solid support with a corresponding capture reagent having a signal generating material is referred to herein as a “detection device” or “kit”.
  • the kit can also include instructions for using the devices and reagents, handling the sample, and analyzing the data. Further the kit may be used with a computer system or software to analyze and report the result of the analysis of the biological sample.
  • kits can also contain one or more reagents (e.g., solubilization buffers, detergents, washes, or buffers) for processing a biological sample.
  • reagents e.g., solubilization buffers, detergents, washes, or buffers
  • Any of the kits described herein can also include, e.g., buffers, blocking agents, mass spectrometry matrix materials, antibody capture agents, positive control samples, negative control samples, software and information such as protocols, guidance and reference data.
  • kits for the analysis of CV event risk status include PCR primers for one or more SOMAmers specific to biomarkers selected from Table 1, Col. 7.
  • the kit may further include instructions for use and correlation of the biomarkers with prediction of risk of a CV event.
  • the kit may also include a DNA array containing the complement of one or more of the Somamers specific for the biomarkers selected from Table 1, Col. 7, reagents, and/or enzymes for amplifying or isolating sample DNA.
  • the kits may include reagents for real-time PCR, for example, TaqMan probes and/or primers, and enzymes.
  • a kit can comprise (a) reagents comprising at least capture reagent for quantifying one or more biomarkers in a test sample, wherein said biomarkers comprise the biomarkers set forth in Table 1, Col. 7, or any other biomarkers or biomarkers panels described herein, and optionally (b) one or more algorithms or computer programs for performing the steps of comparing the amount of each biomarker quantified in the test sample to one or more predetermined cutoffs and assigning a score for each biomarker quantified based on said comparison, combining the assigned scores for each biomarker quantified to obtain a total score, comparing the total score with a predetermined score, and using said comparison to determine whether an individual has an increased risk of a CV event.
  • one or more instructions for manually performing the above steps by a human can be provided.
  • a method for diagnosing an individual can comprise the following: 1) collect or otherwise obtain a biological sample; 2) perform an analytical method to detect and measure the biomarker or biomarkers in the panel in the biological sample; 3) perform any data normalization or standardization required for the method used to collect biomarker values; 4) calculate the marker score; 5) combine the marker scores to obtain a total diagnostic or predictive score; and 6) report the individual's diagnostic or predictive score.
  • the diagnostic or predictive score may be a single number determined from the sum of all the marker calculations that is compared to a preset threshold value that is an indication of the presence or absence of disease.
  • the diagnostic or predictive score may be a series of bars that each represent a biomarker value and the pattern of the responses may be compared to a pre-set pattern for determination of the presence or absence of disease, condition or the increased risk (or not) of an event.
  • FIG. 4 An example of a computer system 100 is shown in FIG. 4 .
  • system 100 is shown comprised of hardware elements that are electrically coupled via bus 108 , including a processor 101 , input device 102 , output device 103 , storage device 104 , computer-readable storage media reader 105 a , communications system 106 , processing acceleration (e.g., DSP or special-purpose processors) 107 and memory 109 .
  • Computer-readable storage media reader 105 a is further coupled to computer-readable storage media 105 b , the combination comprehensively representing remote, local, fixed and/or removable storage devices plus storage media, memory, etc.
  • System 100 for temporarily and/or more permanently containing computer-readable information, which can include storage device 104 , memory 109 and/or any other such accessible system 100 resource.
  • System 100 also comprises software elements (shown as being currently located within working memory 191 ) including an operating system 192 and other code 193 , such as programs, data and the like.
  • system 100 has extensive flexibility and configurability.
  • a single architecture might be utilized to implement one or more servers that can be further configured in accordance with currently desirable protocols, protocol variations, extensions, etc.
  • embodiments may well be utilized in accordance with more specific application requirements.
  • one or more system elements might be implemented as sub-elements within a system 100 component (e.g., within communications system 106 ).
  • Customized hardware might also be utilized and/or particular elements might be implemented in hardware, software or both.
  • connection to other computing devices such as network input/output devices (not shown) may be employed, it is to be understood that wired, wireless, modem, and/or other connection or connections to other computing devices might also be utilized.
  • the system can comprise a database containing features of biomarkers characteristic of prediction of risk of a CV event.
  • the biomarker data (or biomarker information) can be utilized as an input to the computer for use as part of a computer implemented method.
  • the biomarker data can include the data as described herein.
  • system further comprises one or more devices for providing input data to the one or more processors.
  • the system further comprises a memory for storing a data set of ranked data elements.
  • the device for providing input data comprises a detector for detecting the characteristic of the data element, e.g., such as a mass spectrometer or gene chip reader.
  • the system additionally may comprise a database management system.
  • User requests or queries can be formatted in an appropriate language understood by the database management system that processes the query to extract the relevant information from the database of training sets.
  • the system may be connectable to a network to which a network server and one or more clients are connected.
  • the network may be a local area network (LAN) or a wide area network (WAN), as is known in the art.
  • the server includes the hardware necessary for running computer program products (e.g., software) to access database data for processing user requests.
  • the system may include an operating system (e.g., UNIX or Linux) for executing instructions from a database management system.
  • the operating system can operate on a global communications network, such as the internet, and utilize a global communications network server to connect to such a network.
  • the system may include one or more devices that comprise a graphical display interface comprising interface elements such as buttons, pull down menus, scroll bars, fields for entering text, and the like as are routinely found in graphical user interfaces known in the art.
  • Requests entered on a user interface can be transmitted to an application program in the system for formatting to search for relevant information in one or more of the system databases.
  • Requests or queries entered by a user may be constructed in any suitable database language.
  • the graphical user interface may be generated by a graphical user interface code as part of the operating system and can be used to input data and/or to display inputted data.
  • the result of processed data can be displayed in the interface, printed on a printer in communication with the system, saved in a memory device, and/or transmitted over the network or can be provided in the form of the computer readable medium.
  • the system can be in communication with an input device for providing data regarding data elements to the system (e.g., expression values).
  • the input device can include a gene expression profiling system including, e.g., a mass spectrometer, gene chip or array reader, and the like.
  • the methods and apparatus for analyzing CV event risk prediction biomarker information may be implemented in any suitable manner, for example, using a computer program operating on a computer system.
  • a conventional computer system comprising a processor and a random access memory, such as a remotely-accessible application server, network server, personal computer or workstation may be used.
  • Additional computer system components may include memory devices or information storage systems, such as a mass storage system and a user interface, for example a conventional monitor, keyboard and tracking device.
  • the computer system may be a stand-alone system or part of a network of computers including a server and one or more databases.
  • the CV event risk prediction biomarker analysis system can provide functions and operations to complete data analysis, such as data gathering, processing, analysis, reporting and/or diagnosis.
  • the computer system can execute the computer program that may receive, store, search, analyze, and report information relating to the CV event risk prediction biomarkers.
  • the computer program may comprise multiple modules performing various functions or operations, such as a processing module for processing raw data and generating supplemental data and an analysis module for analyzing raw data and supplemental data to generate a CV event risk prediction status and/or diagnosis or risk calculation.
  • Calculation of risk status for a CV event may optionally comprise generating or collecting any other information, including additional biomedical information, regarding the condition of the individual relative to the disease, condition or event, identifying whether further tests may be desirable, or otherwise evaluating the health status of the individual.
  • biomarker information can be retrieved for an individual.
  • the biomarker information can be retrieved from a computer database, for example, after testing of the individual's biological sample is performed.
  • a computer can be utilized to classify each of the biomarker values.
  • a determination can be made as to the likelihood that an individual has increased risk of a CV event based upon a plurality of classifications.
  • the indication can be output to a display or other indicating device so that it is viewable by a person. Thus, for example, it can be displayed on a display screen of a computer or other output device.
  • a computer can be utilized to retrieve biomarker information for an individual.
  • the biomarker information comprises a biomarker value corresponding to a biomarker selected from the group of biomarkers provided in Table 1, Col. 7.
  • a classification of the biomarker value can be performed with the computer.
  • an indication can be made as to the likelihood that the individual has increased risk of a CV event based upon the classification.
  • the indication can be output to a display or other indicating device so that it is viewable by a person. Thus, for example, it can be displayed on a display screen of a computer or other output device.
  • a computer program product may include a computer readable medium having computer readable program code embodied in the medium for causing an application program to execute on a computer with a database.
  • a “computer program product” refers to an organized set of instructions in the form of natural or programming language statements that are contained on a physical media of any nature (e.g., written, electronic, magnetic, optical or otherwise) and that may be used with a computer or other automated data processing system. Such programming language statements, when executed by a computer or data processing system, cause the computer or data processing system to act in accordance with the particular content of the statements.
  • Computer program products include without limitation: programs in source and object code and/or test or data libraries embedded in a computer readable medium.
  • the computer program product that enables a computer system or data processing equipment device to act in pre-selected ways may be provided in a number of forms, including, but not limited to, original source code, assembly code, object code, machine language, encrypted or compressed versions of the foregoing and any and all equivalents.
  • a computer program product for evaluation of the risk of a CV event.
  • a computer program product for indicating a likelihood of risk of a CV event.
  • the computer program product includes a computer readable medium embodying program code executable by a processor of a computing device or system, the program code comprising: code that retrieves data attributed to a biological sample from an individual, wherein the data comprises a biomarker value corresponding to a biomarker in the biological sample selected from the group of biomarkers provided in Table 1, Col. 7; and code that executes a classification method that indicates a CV event risk status of the individual as a function of the biomarker value.
  • the embodiments may be embodied as code stored in a computer-readable memory of virtually any kind including, without limitation, RAM, ROM, magnetic media, optical media, or magneto-optical media. Even more generally, the embodiments could be implemented in software, or in hardware, or any combination thereof including, but not limited to, software running on a general purpose processor, microcode, PLAs, or ASICs.
  • embodiments could be accomplished as computer signals embodied in a carrier wave, as well as signals (e.g., electrical and optical) propagated through a transmission medium.
  • signals e.g., electrical and optical
  • the various types of information discussed above could be formatted in a structure, such as a data structure, and transmitted as an electrical signal through a transmission medium or stored on a computer readable medium.
  • biomarker identification process The biomarker identification process, the utilization of the biomarkers disclosed herein, and the various methods for determining biomarker values are described in detail above with respect to evaluation of risk of a CV event. However, the application of the process, the use of identified biomarkers, and the methods for determining biomarker values are fully applicable to other specific types of cardiovascular conditions, to any other disease or medical condition, or to the identification of individuals who may or may not be benefited by an ancillary medical treatment.
  • This example describes the multiplex aptamer assay used to analyze the samples and controls for the identification of the biomarkers set forth in Table 1.
  • the general protocol for analysis of a sample is illustrated in FIGS. 1A and 1B .
  • the Cox proportional hazard model is employed to produce a risk score from multiple covariates of pathological state.
  • the biomarker values are combined as shown in FIG. 1B by taking the log ratio of the biomarker measurements relative to the normal levels.
  • the Cox model uses the exponential of the weighted sum of these log ratios to produce an estimate of the hazard ratio to the normal population.
  • Custom stock aptamer solutions for 5%, 0.316% and 0.01% serum were prepared at 2 ⁇ concentration in 1 ⁇ SB17, 0.05% Tween-20. These solutions are stored at ⁇ 20° C. until use.
  • each aptamer mix was thawed at 37° C. for 10 minutes, placed in a boiling water bath for 10 minutes and allowed to cool to 25° C. for 20 minutes with vigorous mixing in between each heating step.
  • 550 of each 2 ⁇ aptamer mix was manually pipetted into a 96-well Hybaid plate and the plate foil sealed.
  • the final result was three, 96-well, foil-sealed Hybaid plates with 5%, 0.316% or 0.01% aptamer mixes.
  • the individual aptamer concentration was 2 ⁇ final or 1 nM.
  • a 10% sample solution (2 ⁇ final) was prepared by transferring 8 ⁇ L of sample using a 50 ⁇ L 8-channel spanning pipettor into 96-well Hybaid plates, each well containing 72 ⁇ L of the appropriate sample diluent at 4° C. (1 ⁇ SB17, 0.06% Tween-20, 11.1 ⁇ M Z-block — 2, 0.44 mM MgCl 2 , 2.2 mM AEBSF, 1.1 mM EGTA, 55.6 uM EDTA for serum). This plate was stored on ice until the next sample dilution steps were initiated on the Biomek FxP robot.
  • the 10% sample plate was briefly centrifuged and placed on the Biomek FxP where it was mixed by pipetting up and down with the 96-well pipettor.
  • a ⁇ 0.632% sample plate (2 ⁇ final) was then prepared by transferring 6 ⁇ L of the 10% sample plate into 89 ⁇ L of 1 ⁇ SB17, 0.05% Tween-20 with 2 mM AEBSF.
  • dilution of 6 ⁇ L of the resultant 0.632% sample into 184 ⁇ L of 1 ⁇ SB17, 0.05% Tween-20 made a 0.02% sample plate (2 ⁇ final). Dilutions were done on the Beckman Biomek FxP. After each transfer, the solutions were mixed by pipetting up and down.
  • the 3 sample dilution plates were then transferred to their respective aptamer solutions by adding 55 ⁇ L of the sample to 55 ⁇ L of the appropriate 2 ⁇ aptamer mix.
  • the sample and aptamer solutions were mixed on the robot by pipetting up and down.
  • sample/aptamer plates were sealed with silicon cap mats and placed into a 37° C. incubator for 3.5 hours before proceeding to the Catch 1 step.
  • the cytomat was loaded with all tips, plates, all reagents in troughs (except NHS-biotin reagent which was prepared fresh right before addition to the plates), 3 prepared catch 1 filter plates and 1 prepared MyOne plate.
  • the sample/aptamer plates were removed from the incubator, centrifuged for about 1 minute, cap mat covers removed, and placed on the deck of the Beckman Biomek FxP.
  • the Beckman Biomek FxP program was initiated. All subsequent steps in Catch 1 were performed by the Beckman Biomek FxP robot unless otherwise noted. Within the program, the vacuum was applied to the Catch 1 filter plates to remove the bead supernatant.
  • One hundred microlitres of each of the 5%, 0.316% and 0.01% equilibration binding reactions were added to their respective Catch 1 filtration plates, and each plate was mixed using an on-deck orbital shaker at 800 rpm for 10 minutes.
  • the tagging reaction was removed by vacuum filtration and the reaction quenched by the addition of 150 ⁇ L, of 20 mM glycine in 1 ⁇ SB17, 0.05% Tween-20 to the Catch 1 plates
  • the glycine solution was removed via vacuum filtration and another 1500 ⁇ L of 20 mM glycine (in 1 ⁇ SB17, 0.05% Tween-20) was added to each plate and incubated for 1 minute on orbital shakers at 800 rpm before removal by vacuum filtration.
  • the wells of the Catch 1 plates were subsequently washed by adding 190 ⁇ L 1 ⁇ SB17, 0.05% Tween-20, followed immediately by vacuum filtration and then by adding 190 ⁇ L 1 ⁇ SB17, 0.05% Tween-20 with shaking for 1 minute at 800 rpm before vacuum filtration. These two wash steps were repeated two more times with the exception that the last wash was not removed by vacuum filtration. After the last wash the plates were placed on top of a 1 mL deep-well plate and removed from the deck for centrifugation at 1000 rpm for 1 minute to remove as much extraneous volume from the agarose beads before elution as possible.
  • the plates were placed back onto the Beckman Biomek FxP and 85 ⁇ L of 10 mM DxSO 4 in 1 ⁇ SB17, 0.05% Tween-20 was added to each well of the filter plates.
  • the filter plates were removed from the deck, placed onto a Variomag Thermoshaker (Thermo Fisher Scientific, Inc., Waltham, Mass.) under the BlackRay (Ted Pella, Inc., Redding, Calif.) light sources, and irradiated for 5 minutes while shaking at 800 rpm After the 5-minute incubation the plates were rotated 180 degrees and irradiated with shaking for 5 minutes more.
  • Variomag Thermoshaker Thermo Fisher Scientific, Inc., Waltham, Mass.
  • BlackRay Ted Pella, Inc., Redding, Calif.
  • the photocleaved solutions were sequentially eluted from each Catch 1 plate into a common deep well plate by first placing the 5% Catch 1 filter plate on top of a 1 mL deep-well plate and centrifuging at 1000 rpm for 1 minute. The 0.316% and 0.01% Catch 1 plates were then sequentially centrifuged into the same deep well plate.
  • the robot transferred all of the photo-cleaved eluate from the 1 mL deep-well plate onto the Hybaid plate containing the previously prepared Catch 2 MyOne magnetic beads (after removal of the MyOne buffer via magnetic separation).
  • the solution was incubated while shaking at 1350 rpm for 5 minutes at 25° C. on a Variomag Thermoshaker (Thermo Fisher Scientific, Inc., Waltham, Mass.).
  • the robot transferred the plate to the on deck magnetic separator station.
  • the plate was incubated on the magnet for 90 seconds before removal and discarding of the supernatant.
  • the Catch 2 plate was moved to the on-deck thermal shaker and 75 ⁇ L, of 1 ⁇ SB17, 0.05% Tween-20 was transferred to each well.
  • the plate was mixed for 1 minute at 1350 rpm and 37° C. to resuspend and warm the beads.
  • 75 ⁇ L of 60% glycerol at 37° C. was transferred and the plate continued to mix for another minute at 1350 rpm and 3° C.
  • the robot transferred the plate to the 37° C. magnetic separator where it was incubated on the magnet for 2 minutes and then the robot removed and discarded the supernatant. These washes were repeated two more times.
  • the Catch 2 beads were washed a final time using 150 ⁇ L 1 ⁇ SB19, 0.05% Tween-20 with incubation for 1 minute while shaking at 1350 rpm, prior to magnetic separation.
  • the aptamers were eluted from Catch 2 beads by adding 105 ⁇ L of 100 mM CAPSO with 1 M NaCl, 0.05% Tween-20 to each well. The beads were incubated with this solution with shaking at 1300 rpm for 5 minutes.
  • the Catch 2 plate was then placed onto the magnetic separator for 90 seconds prior to transferring 63 ⁇ L, of the eluate to a new 96-well plate containing 7 ⁇ L, of 500 mM HCl, 500 mM HEPES, 0.05% Tween-20 in each well. After transfer, the solution was mixed robotically by pipetting 60 ⁇ L, up and down five times.
  • the Beckman Biomek FxP transferred 20 ⁇ L, of the neutralized Catch 2 eluate to a fresh Hybaid plate, and 6 ⁇ L, of 10 ⁇ Agilent Block, containing a 10 ⁇ spike of hybridization controls, was added to each well.
  • 30 ⁇ L, of 2 ⁇ Agilent Hybridization buffer was manually pipetted to the each well of the plate containing the neutralized samples and blocking buffer and the solution was mixed by manually pipetting 25 ⁇ L up and down 15 times slowly to avoid extensive bubble formation.
  • the plate was spun at 1000 rpm for 1 minute.
  • Custom Agilent microarray slides (Agilent Technologies, Inc., Santa Clara, Calif.) were designed to contain probes complementary to the aptamer random region plus some primer region. For the majority of the aptamers, the optimal length of the complementary sequence was empirically determined and ranged between 40-50 nucleotides. For later aptamers a 46-mer complementary region was chosen by default. The probes were linked to the slide surface with a poly-T linker for a total probe length of 60 nucleotides.
  • a gasket slide was placed into an Agilent hybridization chamber and 40 ⁇ L of each of the samples containing hybridization and blocking solution was manually pipetted into each gasket.
  • An 8-channel variable spanning pipettor was used in a manner intended to minimize bubble formation.
  • the top of the hybridization chambers were placed onto the slide/backing sandwich and clamping brackets slid over the whole assembly. These assemblies were tightly clamped by turning the screws securely.
  • the assembled hybridization chambers were incubated in an Agilent hybridization oven for 19 hours at 60° C. rotating at 20 rpm.
  • a staining dish for Agilent Wash 2 was prepared by placing a stir bar into an empty glass staining dish.
  • a fourth glass staining dish was set aside for the final acetonitrile wash.
  • Each of six hybridization chambers was disassembled. One-by-one, the slide/backing sandwich was removed from its hybridization chamber and submerged into the staining dish containing Wash 1. The slide/backing sandwich was pried apart using a pair of tweezers, while still submerging the microarray slide. The slide was quickly transferred into the slide rack in the Wash 1 staining dish on the magnetic stir plate.
  • the slide rack was gently raised and lowered 5 times.
  • the magnetic stirrer was turned on at a low setting and the slides incubated for 5 minutes.
  • wash Buffer 2 pre-warmed to 37° C. in an incubator was added to the second prepared staining dish.
  • the slide rack was quickly transferred to Wash Buffer 2 and any excess buffer on the bottom of the rack was removed by scraping it on the top of the stain dish.
  • the slide rack was gently raised and lowered 5 times.
  • the magnetic stirrer was turned on at a low setting and the slides incubated for 5 minutes.
  • the slide rack was slowly pulled out of Wash 2, taking approximately 15 seconds to remove the slides from the solution.
  • acetonitrile ACN
  • the slide rack was transferred to the acetonitrile stain dish.
  • the slide rack was gently raised and lowered 5 times.
  • the magnetic stirrer was turned on at a low setting and the slides incubated for 5 minutes.
  • the slide rack was slowly pulled out of the ACN stain dish and placed on an absorbent towel. The bottom edges of the slides were quickly dried and the slide was placed into a clean slide box.
  • microarray slides were placed into Agilent scanner slide holders and loaded into the Agilent Microarray scanner according to the manufacturer's instructions.
  • the slides were imaged in the Cy3-channel at 5 ⁇ m resolution at the 100% PMT setting and the XRD option enabled at 0.05.
  • the resulting tiff images were processed using Agilent feature extraction software version 10.5.
  • CV event biomarkers The identification of potential CV event biomarkers was performed for prediction of risk of a CV event in a population of individuals in the San Francisco Bay Area. Participants had to meet one of the following enrollment criteria for this study: prior myocardial infarction, angiographic evidence of greater than 50% stenosis in 1 or more coronary vessels, exercise-induced ischemia by treadmill or nuclear testing, or prior coronary revascularization. Exclusion criteria included recent myocardial infarction, inability to walk around 1 block, and plans to relocate. Fasting blood samples were collected, and serum and plasma aliquots were stored at ⁇ 70° C. The multiplexed SOMAmer affinity assay as described in Example 1 was used to measure and report the RFU value for 1034 analytes in each of these 987 samples.
  • PCA In order to identify a set of biomarkers associated with occurrence of events, the combined set of control and early event samples were analyzed using PCA.
  • PCA displays the samples with respect to the axes defined by the strongest variations between all the samples, without regard to the case or control outcome, thus mitigating the risk of overfitting the distinction between case and control.
  • biomarkers can be analyzed for those components of difference between samples which were specific to the separation between the control samples and early event samples.
  • the dimensionality reduction is performed on the control set alone, to determine the multivariate multidimensional space of variation spanned by the differences between control samples, both the samples in the control set and the set of early event samples are deflated for space of variation determined between control samples, the residual variation is enriched in those components separating case from control. This is known as the DSGA method. Separation of cases from early events can be observed along the horizontal axis ( FIG. 2B ) (Nicolau M, Tibshirani R, B ⁇ rresen-Dale A L, Jeffrey S S. Disease-specific genomic analysis: Identifying the signature of pathologic biology. Bioinformatics. 2007; 23:957-965.)
  • Cox proportional hazard model (Cox, David R (1972). “Regression Models and Life-Tables”. Journal of the Royal Statistical Society. Series B (Methodological) 34 (2): 187-220)) is widely used in medical statistics. Cox regression avoids fitting a specific function of time to the cumulative survival, and instead employs a model of relative risk referred to a baseline hazard function (which may vary with time).
  • the baseline hazard function describes the common shape of the survival time distribution for all individuals, while the relative risk gives the level of the hazard for a set of covariate values (such as a single individual or group), as a multiple of the baseline hazard.
  • the relative risk is constant with time in the Cox model.
  • the method involved fitting 1092 simple univariate Cox models to all signals. Forty-six proteins have P-values (Wald, Abraham. (1943). A Method of Estimating Plane Vulnerability Based on Damage of Survivors. Statistical Research Group, Columbia University)) better than 10 ⁇ 14 .
  • the large number of highly significant proteins is at first surprising, however the involvement of the kidney in the cardiovascular disease implies changes in the glomerular filtration rate (GFR). Decreases in GFR will increase all proteins with non-zero renal clearance.
  • the next step filtered the 20 proteins down to nine by requiring that the P-value should be more significant than 0.01. This step suppresses covariant proteins and allows independent proteins to contribute.
  • a final adjustment was made to the biomarker selection in that C9, a member of the membrane attack complex in the final common pathway of the complement system, was judged to be too unspecific in its signaling, a matter which cannot be decided from this study alone, since the study is created to cleanly demonstrate CV event risk.
  • C9 was removed and all the remaining proteins were evaluated in its place. The substitute proteins were ranked on the improvement in the Wald test score, and KLK3.SerpinA3 was close to as effective as C9.
  • Cardiovascular events largely fall into two classes: thrombotic and CHF. Distinguishing between thrombotic and CHF risk has medical utility in guiding therapy, choosing between anti-thrombotic and diuretic medications, for example. Although much of the biology is shared between the thrombotic and CHF classes of events, thrombotic events specifically involve the biology of blood coagulation (as implied by the name thrombotic). Using the ten proteins of Table 3 identified in the Cox proportional hazard model (Example 3), it was possible to look for the signals linked to coagulation and to signals linked to tissue remodeling. To determine any differential signal between the CHF and thrombotic events the relevant Kaplan Meier curves were plotted separately for CHF and thrombotic events.
  • FIG. 8A shows a strong association of thrombotic event free survival with the level of GPVI, plotted as quartiles of the population distribution.
  • FIG. 8B shows that the quartiles of GPVI are not associated with event free survival for CHF events.
  • MATN2 matrix 2
  • FIG. 9A shows that the quartiles of MATN2 are not associated with risk for thrombotic events, while FIG. 9B shows a strong association between MATN2 and CHF events.
  • the event free survival for those individuals with a MATN2 in the 4th quartile of the population is markedly worse than the first three quartiles.
  • FIG. 10 shows that in those subjects on statins, angiopoietin 2 is still strongly useful for prediction of a CV event in high risk individuals.
  • FIG. 10 shows Kaplan Meier plots of all 538 subjects taking statin medication and illustrates that those individuals in the 4th quartile of the population distribution for angiopoietin-2, suffer cardiovascular events at an increased rate compared to those not in the 4th Quartile for angiopoietin-2.
  • angiopoietin-2 is a useful biomarker of the risk of cardiovascular events.
  • FIG. 11 shows that in those subjects on statins, CHRDL1 is also associated with the risk of cardiovascular events in this high risk population.
  • FIG. 11 shows Kaplan Meier plots of all 538 subjects taking statin medication. It illustrates that CHRDL1 is associated with the event free survival of cardiovascular events in individuals treated with statin medications. Thus, despite the effects of treatment with statins, CHRDL1 is a useful biomarker of the risk of cardiovascular events.

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